Attention-enhanced dilated convolution for Parkinson’s disease detection using transcranial sonography
Background Transcranial sonography (TCS) plays a crucial role in diagnosing Parkinson's disease. However, the intricate nature of TCS pathological features, the lack of consistent diagnostic criteria, and the dependence on physicians' expertise can hinder accurate diagnosis. Current TCS-ba...
Saved in:
| Published in | Biomedical engineering online Vol. 23; no. 1; pp. 76 - 20 |
|---|---|
| Main Authors | , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
London
BioMed Central
31.07.2024
BioMed Central Ltd BMC |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1475-925X 1475-925X |
| DOI | 10.1186/s12938-024-01265-5 |
Cover
| Abstract | Background
Transcranial sonography (TCS) plays a crucial role in diagnosing Parkinson's disease. However, the intricate nature of TCS pathological features, the lack of consistent diagnostic criteria, and the dependence on physicians' expertise can hinder accurate diagnosis. Current TCS-based diagnostic methods, which rely on machine learning, often involve complex feature engineering and may struggle to capture deep image features. While deep learning offers advantages in image processing, it has not been tailored to address specific TCS and movement disorder considerations. Consequently, there is a scarcity of research on deep learning algorithms for TCS-based PD diagnosis.
Methods
This study introduces a deep learning residual network model, augmented with attention mechanisms and multi-scale feature extraction, termed AMSNet, to assist in accurate diagnosis. Initially, a multi-scale feature extraction module is implemented to robustly handle the irregular morphological features and significant area information present in TCS images. This module effectively mitigates the effects of artifacts and noise. When combined with a convolutional attention module, it enhances the model's ability to learn features of lesion areas. Subsequently, a residual network architecture, integrated with channel attention, is utilized to capture hierarchical and detailed textures within the images, further enhancing the model's feature representation capabilities.
Results
The study compiled TCS images and personal data from 1109 participants. Experiments conducted on this dataset demonstrated that AMSNet achieved remarkable classification accuracy (92.79%), precision (95.42%), and specificity (93.1%). It surpassed the performance of previously employed machine learning algorithms in this domain, as well as current general-purpose deep learning models.
Conclusion
The AMSNet proposed in this study deviates from traditional machine learning approaches that necessitate intricate feature engineering. It is capable of automatically extracting and learning deep pathological features, and has the capacity to comprehend and articulate complex data. This underscores the substantial potential of deep learning methods in the application of TCS images for the diagnosis of movement disorders.
Graphical Abstract |
|---|---|
| AbstractList | BackgroundTranscranial sonography (TCS) plays a crucial role in diagnosing Parkinson's disease. However, the intricate nature of TCS pathological features, the lack of consistent diagnostic criteria, and the dependence on physicians' expertise can hinder accurate diagnosis. Current TCS-based diagnostic methods, which rely on machine learning, often involve complex feature engineering and may struggle to capture deep image features. While deep learning offers advantages in image processing, it has not been tailored to address specific TCS and movement disorder considerations. Consequently, there is a scarcity of research on deep learning algorithms for TCS-based PD diagnosis.MethodsThis study introduces a deep learning residual network model, augmented with attention mechanisms and multi-scale feature extraction, termed AMSNet, to assist in accurate diagnosis. Initially, a multi-scale feature extraction module is implemented to robustly handle the irregular morphological features and significant area information present in TCS images. This module effectively mitigates the effects of artifacts and noise. When combined with a convolutional attention module, it enhances the model's ability to learn features of lesion areas. Subsequently, a residual network architecture, integrated with channel attention, is utilized to capture hierarchical and detailed textures within the images, further enhancing the model's feature representation capabilities.ResultsThe study compiled TCS images and personal data from 1109 participants. Experiments conducted on this dataset demonstrated that AMSNet achieved remarkable classification accuracy (92.79%), precision (95.42%), and specificity (93.1%). It surpassed the performance of previously employed machine learning algorithms in this domain, as well as current general-purpose deep learning models.ConclusionThe AMSNet proposed in this study deviates from traditional machine learning approaches that necessitate intricate feature engineering. It is capable of automatically extracting and learning deep pathological features, and has the capacity to comprehend and articulate complex data. This underscores the substantial potential of deep learning methods in the application of TCS images for the diagnosis of movement disorders. Transcranial sonography (TCS) plays a crucial role in diagnosing Parkinson's disease. However, the intricate nature of TCS pathological features, the lack of consistent diagnostic criteria, and the dependence on physicians' expertise can hinder accurate diagnosis. Current TCS-based diagnostic methods, which rely on machine learning, often involve complex feature engineering and may struggle to capture deep image features. While deep learning offers advantages in image processing, it has not been tailored to address specific TCS and movement disorder considerations. Consequently, there is a scarcity of research on deep learning algorithms for TCS-based PD diagnosis. This study introduces a deep learning residual network model, augmented with attention mechanisms and multi-scale feature extraction, termed AMSNet, to assist in accurate diagnosis. Initially, a multi-scale feature extraction module is implemented to robustly handle the irregular morphological features and significant area information present in TCS images. This module effectively mitigates the effects of artifacts and noise. When combined with a convolutional attention module, it enhances the model's ability to learn features of lesion areas. Subsequently, a residual network architecture, integrated with channel attention, is utilized to capture hierarchical and detailed textures within the images, further enhancing the model's feature representation capabilities. The study compiled TCS images and personal data from 1109 participants. Experiments conducted on this dataset demonstrated that AMSNet achieved remarkable classification accuracy (92.79%), precision (95.42%), and specificity (93.1%). It surpassed the performance of previously employed machine learning algorithms in this domain, as well as current general-purpose deep learning models. The AMSNet proposed in this study deviates from traditional machine learning approaches that necessitate intricate feature engineering. It is capable of automatically extracting and learning deep pathological features, and has the capacity to comprehend and articulate complex data. This underscores the substantial potential of deep learning methods in the application of TCS images for the diagnosis of movement disorders. Transcranial sonography (TCS) plays a crucial role in diagnosing Parkinson's disease. However, the intricate nature of TCS pathological features, the lack of consistent diagnostic criteria, and the dependence on physicians' expertise can hinder accurate diagnosis. Current TCS-based diagnostic methods, which rely on machine learning, often involve complex feature engineering and may struggle to capture deep image features. While deep learning offers advantages in image processing, it has not been tailored to address specific TCS and movement disorder considerations. Consequently, there is a scarcity of research on deep learning algorithms for TCS-based PD diagnosis. This study introduces a deep learning residual network model, augmented with attention mechanisms and multi-scale feature extraction, termed AMSNet, to assist in accurate diagnosis. Initially, a multi-scale feature extraction module is implemented to robustly handle the irregular morphological features and significant area information present in TCS images. This module effectively mitigates the effects of artifacts and noise. When combined with a convolutional attention module, it enhances the model's ability to learn features of lesion areas. Subsequently, a residual network architecture, integrated with channel attention, is utilized to capture hierarchical and detailed textures within the images, further enhancing the model's feature representation capabilities. The study compiled TCS images and personal data from 1109 participants. Experiments conducted on this dataset demonstrated that AMSNet achieved remarkable classification accuracy (92.79%), precision (95.42%), and specificity (93.1%). It surpassed the performance of previously employed machine learning algorithms in this domain, as well as current general-purpose deep learning models. The AMSNet proposed in this study deviates from traditional machine learning approaches that necessitate intricate feature engineering. It is capable of automatically extracting and learning deep pathological features, and has the capacity to comprehend and articulate complex data. This underscores the substantial potential of deep learning methods in the application of TCS images for the diagnosis of movement disorders. Background Transcranial sonography (TCS) plays a crucial role in diagnosing Parkinson's disease. However, the intricate nature of TCS pathological features, the lack of consistent diagnostic criteria, and the dependence on physicians' expertise can hinder accurate diagnosis. Current TCS-based diagnostic methods, which rely on machine learning, often involve complex feature engineering and may struggle to capture deep image features. While deep learning offers advantages in image processing, it has not been tailored to address specific TCS and movement disorder considerations. Consequently, there is a scarcity of research on deep learning algorithms for TCS-based PD diagnosis. Methods This study introduces a deep learning residual network model, augmented with attention mechanisms and multi-scale feature extraction, termed AMSNet, to assist in accurate diagnosis. Initially, a multi-scale feature extraction module is implemented to robustly handle the irregular morphological features and significant area information present in TCS images. This module effectively mitigates the effects of artifacts and noise. When combined with a convolutional attention module, it enhances the model's ability to learn features of lesion areas. Subsequently, a residual network architecture, integrated with channel attention, is utilized to capture hierarchical and detailed textures within the images, further enhancing the model's feature representation capabilities. Results The study compiled TCS images and personal data from 1109 participants. Experiments conducted on this dataset demonstrated that AMSNet achieved remarkable classification accuracy (92.79%), precision (95.42%), and specificity (93.1%). It surpassed the performance of previously employed machine learning algorithms in this domain, as well as current general-purpose deep learning models. Conclusion The AMSNet proposed in this study deviates from traditional machine learning approaches that necessitate intricate feature engineering. It is capable of automatically extracting and learning deep pathological features, and has the capacity to comprehend and articulate complex data. This underscores the substantial potential of deep learning methods in the application of TCS images for the diagnosis of movement disorders. Graphical Abstract Background Transcranial sonography (TCS) plays a crucial role in diagnosing Parkinson's disease. However, the intricate nature of TCS pathological features, the lack of consistent diagnostic criteria, and the dependence on physicians' expertise can hinder accurate diagnosis. Current TCS-based diagnostic methods, which rely on machine learning, often involve complex feature engineering and may struggle to capture deep image features. While deep learning offers advantages in image processing, it has not been tailored to address specific TCS and movement disorder considerations. Consequently, there is a scarcity of research on deep learning algorithms for TCS-based PD diagnosis. Methods This study introduces a deep learning residual network model, augmented with attention mechanisms and multi-scale feature extraction, termed AMSNet, to assist in accurate diagnosis. Initially, a multi-scale feature extraction module is implemented to robustly handle the irregular morphological features and significant area information present in TCS images. This module effectively mitigates the effects of artifacts and noise. When combined with a convolutional attention module, it enhances the model's ability to learn features of lesion areas. Subsequently, a residual network architecture, integrated with channel attention, is utilized to capture hierarchical and detailed textures within the images, further enhancing the model's feature representation capabilities. Results The study compiled TCS images and personal data from 1109 participants. Experiments conducted on this dataset demonstrated that AMSNet achieved remarkable classification accuracy (92.79%), precision (95.42%), and specificity (93.1%). It surpassed the performance of previously employed machine learning algorithms in this domain, as well as current general-purpose deep learning models. Conclusion The AMSNet proposed in this study deviates from traditional machine learning approaches that necessitate intricate feature engineering. It is capable of automatically extracting and learning deep pathological features, and has the capacity to comprehend and articulate complex data. This underscores the substantial potential of deep learning methods in the application of TCS images for the diagnosis of movement disorders. Graphical Keywords: Parkinson's disease, Transcranial sonography, Deep learning, Computer-aided diagnosis, Attention mechanisms, Movement disorders Transcranial sonography (TCS) plays a crucial role in diagnosing Parkinson's disease. However, the intricate nature of TCS pathological features, the lack of consistent diagnostic criteria, and the dependence on physicians' expertise can hinder accurate diagnosis. Current TCS-based diagnostic methods, which rely on machine learning, often involve complex feature engineering and may struggle to capture deep image features. While deep learning offers advantages in image processing, it has not been tailored to address specific TCS and movement disorder considerations. Consequently, there is a scarcity of research on deep learning algorithms for TCS-based PD diagnosis.BACKGROUNDTranscranial sonography (TCS) plays a crucial role in diagnosing Parkinson's disease. However, the intricate nature of TCS pathological features, the lack of consistent diagnostic criteria, and the dependence on physicians' expertise can hinder accurate diagnosis. Current TCS-based diagnostic methods, which rely on machine learning, often involve complex feature engineering and may struggle to capture deep image features. While deep learning offers advantages in image processing, it has not been tailored to address specific TCS and movement disorder considerations. Consequently, there is a scarcity of research on deep learning algorithms for TCS-based PD diagnosis.This study introduces a deep learning residual network model, augmented with attention mechanisms and multi-scale feature extraction, termed AMSNet, to assist in accurate diagnosis. Initially, a multi-scale feature extraction module is implemented to robustly handle the irregular morphological features and significant area information present in TCS images. This module effectively mitigates the effects of artifacts and noise. When combined with a convolutional attention module, it enhances the model's ability to learn features of lesion areas. Subsequently, a residual network architecture, integrated with channel attention, is utilized to capture hierarchical and detailed textures within the images, further enhancing the model's feature representation capabilities.METHODSThis study introduces a deep learning residual network model, augmented with attention mechanisms and multi-scale feature extraction, termed AMSNet, to assist in accurate diagnosis. Initially, a multi-scale feature extraction module is implemented to robustly handle the irregular morphological features and significant area information present in TCS images. This module effectively mitigates the effects of artifacts and noise. When combined with a convolutional attention module, it enhances the model's ability to learn features of lesion areas. Subsequently, a residual network architecture, integrated with channel attention, is utilized to capture hierarchical and detailed textures within the images, further enhancing the model's feature representation capabilities.The study compiled TCS images and personal data from 1109 participants. Experiments conducted on this dataset demonstrated that AMSNet achieved remarkable classification accuracy (92.79%), precision (95.42%), and specificity (93.1%). It surpassed the performance of previously employed machine learning algorithms in this domain, as well as current general-purpose deep learning models.RESULTSThe study compiled TCS images and personal data from 1109 participants. Experiments conducted on this dataset demonstrated that AMSNet achieved remarkable classification accuracy (92.79%), precision (95.42%), and specificity (93.1%). It surpassed the performance of previously employed machine learning algorithms in this domain, as well as current general-purpose deep learning models.The AMSNet proposed in this study deviates from traditional machine learning approaches that necessitate intricate feature engineering. It is capable of automatically extracting and learning deep pathological features, and has the capacity to comprehend and articulate complex data. This underscores the substantial potential of deep learning methods in the application of TCS images for the diagnosis of movement disorders.CONCLUSIONThe AMSNet proposed in this study deviates from traditional machine learning approaches that necessitate intricate feature engineering. It is capable of automatically extracting and learning deep pathological features, and has the capacity to comprehend and articulate complex data. This underscores the substantial potential of deep learning methods in the application of TCS images for the diagnosis of movement disorders. Abstract Background Transcranial sonography (TCS) plays a crucial role in diagnosing Parkinson's disease. However, the intricate nature of TCS pathological features, the lack of consistent diagnostic criteria, and the dependence on physicians' expertise can hinder accurate diagnosis. Current TCS-based diagnostic methods, which rely on machine learning, often involve complex feature engineering and may struggle to capture deep image features. While deep learning offers advantages in image processing, it has not been tailored to address specific TCS and movement disorder considerations. Consequently, there is a scarcity of research on deep learning algorithms for TCS-based PD diagnosis. Methods This study introduces a deep learning residual network model, augmented with attention mechanisms and multi-scale feature extraction, termed AMSNet, to assist in accurate diagnosis. Initially, a multi-scale feature extraction module is implemented to robustly handle the irregular morphological features and significant area information present in TCS images. This module effectively mitigates the effects of artifacts and noise. When combined with a convolutional attention module, it enhances the model's ability to learn features of lesion areas. Subsequently, a residual network architecture, integrated with channel attention, is utilized to capture hierarchical and detailed textures within the images, further enhancing the model's feature representation capabilities. Results The study compiled TCS images and personal data from 1109 participants. Experiments conducted on this dataset demonstrated that AMSNet achieved remarkable classification accuracy (92.79%), precision (95.42%), and specificity (93.1%). It surpassed the performance of previously employed machine learning algorithms in this domain, as well as current general-purpose deep learning models. Conclusion The AMSNet proposed in this study deviates from traditional machine learning approaches that necessitate intricate feature engineering. It is capable of automatically extracting and learning deep pathological features, and has the capacity to comprehend and articulate complex data. This underscores the substantial potential of deep learning methods in the application of TCS images for the diagnosis of movement disorders. Graphical Abstract |
| ArticleNumber | 76 |
| Audience | Academic |
| Author | Wan, Linlin Liu, Jing Chen, Shuang Jiang, Hong Shi, Yuting Qiu, Rong Wan, Yongyan |
| Author_xml | – sequence: 1 givenname: Shuang surname: Chen fullname: Chen, Shuang organization: School of Computer Science and Engineering, Central South University – sequence: 2 givenname: Yuting surname: Shi fullname: Shi, Yuting organization: Department of Neurology, Xiangya Hospital, Central South University, Key Laboratory of Hunan Province in Neurodegenerative Disorders, Central South University – sequence: 3 givenname: Linlin surname: Wan fullname: Wan, Linlin organization: Department of Neurology, Xiangya Hospital, Central South University, Key Laboratory of Hunan Province in Neurodegenerative Disorders, Central South University, National Clinical Research Center for Geriatric Disorders, Xiangya Hospital,, Central South University, National International Collaborative Research Center for Medical Metabolomics, Central South University – sequence: 4 givenname: Jing surname: Liu fullname: Liu, Jing organization: School of Computer Science and Engineering, Central South University – sequence: 5 givenname: Yongyan surname: Wan fullname: Wan, Yongyan organization: School of Computer Science and Engineering, Central South University – sequence: 6 givenname: Hong surname: Jiang fullname: Jiang, Hong organization: Department of Neurology, Xiangya Hospital, Central South University, Key Laboratory of Hunan Province in Neurodegenerative Disorders, Central South University, Hunan International Scientific and Technological Cooperation Base of Neurodegenerative and Neurogenetic Diseases, National Clinical Research Center for Geriatric Disorders, Xiangya Hospital,, Central South University, National International Collaborative Research Center for Medical Metabolomics, Central South University – sequence: 7 givenname: Rong surname: Qiu fullname: Qiu, Rong email: qiurongrong@126.com organization: School of Computer Science and Engineering, Central South University |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/39085884$$D View this record in MEDLINE/PubMed |
| BookMark | eNp9kstu1DAUhiNURC_wAixQJDawSOtL7LFXaFRxGakSFReJneU4dsZDxh7spKI7XoPX40k4mSltByEUKY7s7_zO-c9_XByEGGxRPMXoFGPBzzImkooKkbpCmHBWsQfFEa5nrJKEfTm4931YHOe8QoggxOWj4pBKJJgQ9VGxnA-DDYOPobJhqYOxbdn6Xg-wmhiuYj9Oh6WLqbzU6asPOYZfP35moLLV2ZatHazZMmP2oSuHpEM28PK6LwGOXdKb5fXj4qHTfbZPbtaT4vOb15_O31UX798uzucXlWGSDlXrpIGmaGO4Q4RILh3imDrXUlMzzaXFhjg201QYy_Ws1Y2guHa4EawhHNGTYrHTbaNeqU3ya52uVdRebTdi6pROgze9VbR1mjWiIcbxWvBGEtfAXcQYhrh2DLRe7bQ2Y7O2rQGjku73RPdPgl-qLl4pDJNBhE1_8-JGIcVvo82DWvtsbN_rYOOYFUWCS8awmAH6_C90FccUwCug5DRqROs7qtPQgQ8uwsVmElVzAQAmoqZAnf6Dgqe1aw9jtc7D_l7By70CYAb7fej0mLNafPywzz6778qtHX9CBQDZASbFnJN1twhGakqu2iVXQXLVNrlq8pruijLAobPprv3_VP0GQ4Txiw |
| Cites_doi | 10.1038/s41598-023-36311-0 10.1038/s41598-022-27266-9 10.1016/S1474-4422(21)00030-2 10.1016/j.neucom.2018.09.025 10.1212/WNL.45.1.182 10.1016/j.ultrasmedbio.2015.09.009 10.1016/j.jns.2022.120220 10.1016/j.patrec.2020.03.007 10.1097/CM9.0000000000001503 10.1002/jum.14528 10.1016/j.ins.2022.07.044 10.1121/1.5147329 10.1007/s10072-023-07154-4 10.1186/s41983-023-00732-5 10.1016/j.ejmp.2021.02.006 10.1016/j.neucom.2021.02.070 10.1016/j.ultrasmedbio.2012.07.017 10.3233/JPD-181474 10.3934/mbe.2019280 10.1109/TPAMI.2017.2699184 10.1016/j.compbiomed.2023.106791 10.1016/S0140-6736(23)01419-8 10.1007/s12559-019-09691-7 10.1016/j.parkreldis.2017.06.006 10.3390/ijms24076573 10.3233/JPD-213012 |
| ContentType | Journal Article |
| Copyright | The Author(s) 2024 2024. The Author(s). COPYRIGHT 2024 BioMed Central Ltd. 2024. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. The Author(s) 2024 2024 |
| Copyright_xml | – notice: The Author(s) 2024 – notice: 2024. The Author(s). – notice: COPYRIGHT 2024 BioMed Central Ltd. – notice: 2024. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. – notice: The Author(s) 2024 2024 |
| DBID | C6C AAYXX CITATION CGR CUY CVF ECM EIF NPM ISR 3V. 7QO 7X7 7XB 88E 8FD 8FE 8FG 8FH 8FI 8FJ 8FK ABJCF ABUWG AFKRA AZQEC BBNVY BENPR BGLVJ BHPHI CCPQU COVID DWQXO FR3 FYUFA GHDGH GNUQQ HCIFZ K9. L6V LK8 M0S M1P M7P M7S P64 PHGZM PHGZT PIMPY PJZUB PKEHL PPXIY PQEST PQGLB PQQKQ PQUKI PRINS PTHSS 7X8 5PM DOA |
| DOI | 10.1186/s12938-024-01265-5 |
| DatabaseName | Springer Nature OA Free Journals CrossRef Medline MEDLINE MEDLINE (Ovid) MEDLINE MEDLINE PubMed Gale In Context: Science ProQuest Central (Corporate) Biotechnology Research Abstracts Health & Medical Collection ProQuest Central (purchase pre-March 2016) Medical Database (Alumni Edition) Technology Research Database ProQuest SciTech Collection ProQuest Technology Collection ProQuest Natural Science Collection Hospital Premium Collection Hospital Premium Collection (Alumni Edition) ProQuest Central (Alumni) (purchase pre-March 2016) Materials Science & Engineering Collection (subscription) ProQuest Central (Alumni Edition) ProQuest Central UK/Ireland ProQuest Central Essentials Biological Science Collection (subscription) ProQuest Central Technology Collection Natural Science Collection ProQuest One Community College Coronavirus Research Database ProQuest Central Korea Engineering Research Database Health Research Premium Collection Health Research Premium Collection (Alumni) ProQuest Central Student SciTech Premium Collection ProQuest Health & Medical Complete (Alumni) ProQuest Engineering Collection ProQuest Biological Science Collection Health & Medical Collection (Alumni Edition) Medical Database Biological Science Database Engineering Database (subscription) Biotechnology and BioEngineering Abstracts ProQuest Central Premium ProQuest One Academic (New) Publicly Available Content Database ProQuest Health & Medical Research Collection ProQuest One Academic Middle East (New) ProQuest One Health & Nursing ProQuest One Academic Eastern Edition (DO NOT USE) ProQuest One Applied & Life Sciences ProQuest One Academic ProQuest One Academic UKI Edition ProQuest Central China Engineering Collection MEDLINE - Academic PubMed Central (Full Participant titles) DOAJ Directory of Open Access Journals |
| DatabaseTitle | CrossRef MEDLINE Medline Complete MEDLINE with Full Text PubMed MEDLINE (Ovid) Publicly Available Content Database ProQuest Central Student Technology Collection Technology Research Database ProQuest One Academic Middle East (New) ProQuest Central Essentials ProQuest Health & Medical Complete (Alumni) ProQuest Central (Alumni Edition) SciTech Premium Collection ProQuest One Community College ProQuest One Health & Nursing ProQuest Natural Science Collection ProQuest Central China ProQuest Central ProQuest One Applied & Life Sciences ProQuest Health & Medical Research Collection ProQuest Engineering Collection Health Research Premium Collection Biotechnology Research Abstracts Health and Medicine Complete (Alumni Edition) Natural Science Collection ProQuest Central Korea Health & Medical Research Collection Biological Science Collection ProQuest Central (New) ProQuest Medical Library (Alumni) Engineering Collection Engineering Database ProQuest Biological Science Collection ProQuest One Academic Eastern Edition Coronavirus Research Database ProQuest Hospital Collection ProQuest Technology Collection Health Research Premium Collection (Alumni) Biological Science Database ProQuest SciTech Collection ProQuest Hospital Collection (Alumni) Biotechnology and BioEngineering Abstracts ProQuest Health & Medical Complete ProQuest Medical Library ProQuest One Academic UKI Edition Materials Science & Engineering Collection Engineering Research Database ProQuest One Academic ProQuest One Academic (New) ProQuest Central (Alumni) MEDLINE - Academic |
| DatabaseTitleList | Publicly Available Content Database MEDLINE MEDLINE - Academic |
| Database_xml | – sequence: 1 dbid: C6C name: Springer Nature OA Free Journals url: http://www.springeropen.com/ sourceTypes: Publisher – sequence: 2 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 3 dbid: NPM name: PubMed url: https://proxy.k.utb.cz/login?url=http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed sourceTypes: Index Database – sequence: 4 dbid: EIF name: MEDLINE url: https://proxy.k.utb.cz/login?url=https://www.webofscience.com/wos/medline/basic-search sourceTypes: Index Database – sequence: 5 dbid: 8FG name: ProQuest Technology Collection url: https://search.proquest.com/technologycollection1 sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Medicine Engineering |
| EISSN | 1475-925X |
| EndPage | 20 |
| ExternalDocumentID | oai_doaj_org_article_3dfa5b8b2cf6486b92fb6132cc506af5 PMC11290250 A803412843 39085884 10_1186_s12938_024_01265_5 |
| Genre | Journal Article |
| GeographicLocations | China |
| GeographicLocations_xml | – name: China |
| GrantInformation_xml | – fundername: the clinical research fundation of the National Clinical Research Center for Geriatric Diseases grantid: 2020LNJJ12 – fundername: National Key Research and Development Program of China grantid: 2021YFA0805200 funderid: http://dx.doi.org/10.13039/501100012166 – fundername: the National Natural Science Foundation of China grantid: 82301628; 81974176 and 82171254 – fundername: the Innovation Research Group Project of Natural Science Foundation of Hunan Province grantid: 2020JJ1008 – fundername: the Innovative Research and Development Program of Development and Reform Commission of Hunan Province – fundername: National Key Research and Development Program of China grantid: 2021YFA0805200 – fundername: the National Natural Science Foundation of China grantid: 82301628 – fundername: the National Natural Science Foundation of China grantid: 81974176 and 82171254 |
| GroupedDBID | --- 0R~ 23N 2WC 53G 5GY 5VS 6J9 6PF 7X7 88E 8FE 8FG 8FH 8FI 8FJ AAFWJ AAJSJ AASML AAWTL ABDBF ABJCF ABUWG ACGFO ACGFS ACIHN ACIWK ACPRK ACUHS ADBBV ADMLS ADRAZ ADUKV AEAQA AENEX AFKRA AFPKN AFRAH AHBYD AHMBA AHYZX ALMA_UNASSIGNED_HOLDINGS AMKLP AMTXH AOIJS BAPOH BAWUL BBNVY BCNDV BENPR BFQNJ BGLVJ BHPHI BMC BPHCQ BVXVI C6C CCPQU CS3 DIK E3Z EAD EAP EAS EBD EBLON EBS EMB EMK EMOBN ESX F5P FRP FYUFA GROUPED_DOAJ GX1 HCIFZ HMCUK HYE I-F IAO IGS IHR INH INR ISR ITC KQ8 L6V LK8 M1P M48 M7P M7S MK~ ML~ M~E O5R O5S OK1 OVT P2P PGMZT PHGZM PHGZT PIMPY PJZUB PPXIY PQGLB PQQKQ PROAC PSQYO PTHSS PUEGO RBZ RNS ROL RPM RSV SEG SOJ SV3 TR2 TUS UKHRP W2D WOQ WOW XSB AAYXX ALIPV CITATION CGR CUY CVF ECM EIF NPM PMFND 3V. 7QO 7XB 8FD 8FK AZQEC COVID DWQXO FR3 GNUQQ K9. P64 PKEHL PQEST PQUKI PRINS 7X8 5PM |
| ID | FETCH-LOGICAL-c593t-df9c9383bc6f022969f0613ffd3c45a69e1c2f57a38ce6a7dab8314f1b85b2603 |
| IEDL.DBID | M48 |
| ISSN | 1475-925X |
| IngestDate | Wed Aug 27 01:25:14 EDT 2025 Thu Aug 21 18:33:55 EDT 2025 Fri Sep 05 12:29:11 EDT 2025 Fri Jul 25 19:24:10 EDT 2025 Tue Jun 17 22:04:42 EDT 2025 Tue Jun 10 21:04:29 EDT 2025 Fri Jun 27 06:04:30 EDT 2025 Mon Jul 21 06:03:39 EDT 2025 Tue Jul 01 00:34:40 EDT 2025 Sat Sep 06 07:30:05 EDT 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 1 |
| Keywords | Deep learning Attention mechanisms Transcranial sonography Parkinson’s disease Computer-aided diagnosis Movement disorders |
| Language | English |
| License | 2024. The Author(s). Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c593t-df9c9383bc6f022969f0613ffd3c45a69e1c2f57a38ce6a7dab8314f1b85b2603 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 |
| OpenAccessLink | http://journals.scholarsportal.info/openUrl.xqy?doi=10.1186/s12938-024-01265-5 |
| PMID | 39085884 |
| PQID | 3091293034 |
| PQPubID | 42562 |
| PageCount | 20 |
| ParticipantIDs | doaj_primary_oai_doaj_org_article_3dfa5b8b2cf6486b92fb6132cc506af5 pubmedcentral_primary_oai_pubmedcentral_nih_gov_11290250 proquest_miscellaneous_3086955187 proquest_journals_3091293034 gale_infotracmisc_A803412843 gale_infotracacademiconefile_A803412843 gale_incontextgauss_ISR_A803412843 pubmed_primary_39085884 crossref_primary_10_1186_s12938_024_01265_5 springer_journals_10_1186_s12938_024_01265_5 |
| ProviderPackageCode | CITATION AAYXX |
| PublicationCentury | 2000 |
| PublicationDate | 2024-07-31 |
| PublicationDateYYYYMMDD | 2024-07-31 |
| PublicationDate_xml | – month: 07 year: 2024 text: 2024-07-31 day: 31 |
| PublicationDecade | 2020 |
| PublicationPlace | London |
| PublicationPlace_xml | – name: London – name: England |
| PublicationTitle | Biomedical engineering online |
| PublicationTitleAbbrev | BioMed Eng OnLine |
| PublicationTitleAlternate | Biomed Eng Online |
| PublicationYear | 2024 |
| Publisher | BioMed Central BioMed Central Ltd BMC |
| Publisher_xml | – name: BioMed Central – name: BioMed Central Ltd – name: BMC |
| References | Y Wei (1265_CR40) 2018 A Sakalauskas (1265_CR14) 2016; 42 1265_CR41 G Becker (1265_CR7) 1995; 45 L-S Wang (1265_CR11) 2021; 134 NA Kishk (1265_CR10) 2023; 59 NM Koutroumpa (1265_CR38) 2023; 24 A Shafieesabet (1265_CR9) 2017; 42 Z Xue (1265_CR20) 2018 S Woo (1265_CR42) 2018 A Sakalauskas (1265_CR15) 2018; 37 Y Ben-Shlomo (1265_CR3) 2024; 403 Z Ullah (1265_CR36) 2022; 608 E Tolosa (1265_CR4) 2021; 20 A Srinivas (1265_CR29) 2021 Z Liu (1265_CR31) 2021 1265_CR28 O Pauly (1265_CR12) 2012 N Thirusangu (1265_CR17) 2020; 148 B Gong (1265_CR19) 2018; 320 ON Manzari (1265_CR34) 2023; 157 1265_CR32 G Huang (1265_CR33) 2017 Z Tu (1265_CR27) 2022 L Shen (1265_CR21) 2020; 12 L Hirsch (1265_CR2) 2016; 38 L-C Chen (1265_CR37) 2017; 40 L Chen (1265_CR18) 2012 Z Lu (1265_CR39) 2020; 133 Z Ullah (1265_CR24) 2023; 13 T Hu (1265_CR35) 2021; 443 I Castiglioni (1265_CR25) 2021; 83 CFR Chen (1265_CR30) 2021 YL Mei (1265_CR6) 2021; 2021 J Shi (1265_CR22) 2018 B Heim (1265_CR8) 2022; 12 X Fei (1265_CR16) 2019; 16 E Dorsey (1265_CR1) 2018; 8 H Golan (1265_CR5) 2022; 436 CW Ding (1265_CR23) 2024; 45 Z Ullah (1265_CR26) 2023; 13 A Plate (1265_CR13) 2012; 38 |
| References_xml | – start-page: 443 volume-title: Medical image computing and computer-assisted intervention MICCAI 2012: 15th international conference on medical image computing and computer-assisted intervention year: 2012 ident: 1265_CR12 – volume: 13 start-page: 9087 issue: 1 year: 2023 ident: 1265_CR24 publication-title: Sci Rep doi: 10.1038/s41598-023-36311-0 – volume: 13 start-page: 261 issue: 1 year: 2023 ident: 1265_CR26 publication-title: Sci Rep doi: 10.1038/s41598-022-27266-9 – volume: 20 start-page: 385 issue: 5 year: 2021 ident: 1265_CR4 publication-title: Lancet Neurol doi: 10.1016/S1474-4422(21)00030-2 – start-page: 272 volume-title: Feature analysis for Parkinson’s disease detection based on transcranial sonography image year: 2012 ident: 1265_CR18 – volume: 320 start-page: 141 year: 2018 ident: 1265_CR19 publication-title: Neurocomputing doi: 10.1016/j.neucom.2018.09.025 – start-page: 16519 volume-title: Bottleneck transformers for visual recognition year: 2021 ident: 1265_CR29 – start-page: 10012 volume-title: Swin transformer: Hierarchical vision transformer using shifted windows year: 2021 ident: 1265_CR31 – volume: 45 start-page: 182 issue: 1 year: 1995 ident: 1265_CR7 publication-title: Neurology doi: 10.1212/WNL.45.1.182 – volume: 42 start-page: 322 issue: 1 year: 2016 ident: 1265_CR14 publication-title: Ultrasound Med Biol doi: 10.1016/j.ultrasmedbio.2015.09.009 – start-page: 459 volume-title: Maxvit: multi-axis vision transformer year: 2022 ident: 1265_CR27 – volume: 436 year: 2022 ident: 1265_CR5 publication-title: J Neurol Sci doi: 10.1016/j.jns.2022.120220 – volume: 133 start-page: 173 year: 2020 ident: 1265_CR39 publication-title: Pattern Recogn Lett doi: 10.1016/j.patrec.2020.03.007 – start-page: 357 volume-title: Crossvit: cross-attention multi-scale vision transformer for image classification year: 2021 ident: 1265_CR30 – volume: 134 start-page: 1726 issue: 14 year: 2021 ident: 1265_CR11 publication-title: Chin Med J doi: 10.1097/CM9.0000000000001503 – volume: 37 start-page: 1753 issue: 7 year: 2018 ident: 1265_CR15 publication-title: J Ultrasound Med doi: 10.1002/jum.14528 – start-page: 7268 volume-title: Revisiting dilated convolution: a simple approach for weakly-and semi-supervised semantic segmentation year: 2018 ident: 1265_CR40 – volume: 608 start-page: 1541 year: 2022 ident: 1265_CR36 publication-title: Inf Sci doi: 10.1016/j.ins.2022.07.044 – volume: 148 start-page: 2636 issue: 4 year: 2020 ident: 1265_CR17 publication-title: J Acoust Soc Am doi: 10.1121/1.5147329 – ident: 1265_CR41 – volume: 45 start-page: 2641 issue: 6 year: 2024 ident: 1265_CR23 publication-title: Neurol Sci doi: 10.1007/s10072-023-07154-4 – volume: 59 start-page: 134 issue: 1 year: 2023 ident: 1265_CR10 publication-title: Egypt J Neurol Psychiatr Neurosurg doi: 10.1186/s41983-023-00732-5 – volume: 83 start-page: 9 year: 2021 ident: 1265_CR25 publication-title: Physica Med doi: 10.1016/j.ejmp.2021.02.006 – volume: 443 start-page: 320 year: 2021 ident: 1265_CR35 publication-title: Neurocomputing doi: 10.1016/j.neucom.2021.02.070 – volume: 38 start-page: S203 year: 2016 ident: 1265_CR2 publication-title: Neuroepidemiology – volume: 38 start-page: 2041 issue: 12 year: 2012 ident: 1265_CR13 publication-title: Ultrasound Med Biol doi: 10.1016/j.ultrasmedbio.2012.07.017 – start-page: 3 volume-title: Cbam: convolutional block attention module year: 2018 ident: 1265_CR42 – start-page: 574 volume-title: Transcranial sonography based diagnosis of Parkinson’s disease via cascaded kernel RVFL+ year: 2018 ident: 1265_CR20 – volume: 8 start-page: S3 issue: s1 year: 2018 ident: 1265_CR1 publication-title: J Parkinsons Dis doi: 10.3233/JPD-181474 – volume: 16 start-page: 5640 issue: 5 year: 2019 ident: 1265_CR16 publication-title: Math Biosci Eng doi: 10.3934/mbe.2019280 – volume: 40 start-page: 834 issue: 4 year: 2017 ident: 1265_CR37 publication-title: IEEE Trans Pattern Anal Mach Intell doi: 10.1109/TPAMI.2017.2699184 – ident: 1265_CR28 – ident: 1265_CR32 – volume: 157 year: 2023 ident: 1265_CR34 publication-title: Comput Biol Med doi: 10.1016/j.compbiomed.2023.106791 – volume: 403 start-page: 283 issue: 10423 year: 2024 ident: 1265_CR3 publication-title: Lancet doi: 10.1016/S0140-6736(23)01419-8 – volume: 2021 start-page: 9 year: 2021 ident: 1265_CR6 publication-title: Parkinsons Dis – volume: 12 start-page: 553 year: 2020 ident: 1265_CR21 publication-title: Cogn Comput doi: 10.1007/s12559-019-09691-7 – start-page: 61 volume-title: Multiple kernel learning based classification of Parkinson’s disease with multi-modal transcranial sonography year: 2018 ident: 1265_CR22 – volume: 42 start-page: 1 year: 2017 ident: 1265_CR9 publication-title: Parkinsonism Relat Disord doi: 10.1016/j.parkreldis.2017.06.006 – volume: 24 start-page: 6573 issue: 7 year: 2023 ident: 1265_CR38 publication-title: Int J Mol Sci doi: 10.3390/ijms24076573 – volume: 12 start-page: 1115 issue: 4 year: 2022 ident: 1265_CR8 publication-title: J Parkinsons Dis doi: 10.3233/JPD-213012 – start-page: 4700 volume-title: Densely connected convolutional networks year: 2017 ident: 1265_CR33 |
| SSID | ssj0020069 |
| Score | 2.38006 |
| Snippet | Background
Transcranial sonography (TCS) plays a crucial role in diagnosing Parkinson's disease. However, the intricate nature of TCS pathological features,... Transcranial sonography (TCS) plays a crucial role in diagnosing Parkinson's disease. However, the intricate nature of TCS pathological features, the lack of... Background Transcranial sonography (TCS) plays a crucial role in diagnosing Parkinson's disease. However, the intricate nature of TCS pathological features,... BackgroundTranscranial sonography (TCS) plays a crucial role in diagnosing Parkinson's disease. However, the intricate nature of TCS pathological features, the... Abstract Background Transcranial sonography (TCS) plays a crucial role in diagnosing Parkinson's disease. However, the intricate nature of TCS pathological... |
| SourceID | doaj pubmedcentral proquest gale pubmed crossref springer |
| SourceType | Open Website Open Access Repository Aggregation Database Index Database Publisher |
| StartPage | 76 |
| SubjectTerms | Accuracy Algorithms Analysis Attention mechanisms Biomaterials Biomedical Engineering and Bioengineering Biomedical Engineering/Biotechnology Biotechnology Care and treatment Computer-aided diagnosis Datasets Deep Learning Diagnosis Disease detection Engineering Feature extraction Health aspects Humans Image enhancement Image processing Image Processing, Computer-Assisted - methods Learning algorithms Machine learning Medical diagnosis Medical imaging Methods Modules Movement disorders Neural networks Neurodegenerative diseases Neuroimaging Noise Parkinson Disease - diagnostic imaging Parkinson's disease Performance evaluation Quality of life Research methodology Transcranial sonography Ultrasonic imaging Ultrasonography, Doppler, Transcranial - methods |
| SummonAdditionalLinks | – databaseName: DOAJ Directory of Open Access Journals dbid: DOA link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV3NbtQwELZQDwgOiJa_QEEGIXEAq7tx7NjHbUVVkMoBqNSbZTt2t1KVRSR75zV4PZ6kM46zbIoQFy45xJMonhnnm4lnvhDyOnGqVTVnlZWOVRAzM6cawaSY-1roEKqA3cinn-TJWfXxXJxv_eoLa8IGeuBBcQe8iVY45UofJdzK6TI6gKDSezGTNib20pmejclUTrWQgHdskVHyoENUUwzwCFLnUgomJjCU2Pr_fCdvgdLNgskbu6YJjI7vk3s5iqSL4el3ya3Q7pG7W9yCe-T2ad41f0CWi74fqhpZaJdpy582l1cQZTYUq86z91GIXyl2QaeGsF8_fnY0b9_QJvSpZKulWCd_QXtEOA8H8F4Kwpn3-iE5O37_9eiE5T8sMC8071kTtQfFcOdlBDDXUkfE9xgb7ithpQ5zX0ZRW658kLZurFN8XsW5U8JBJsQfkZ121YYnhEIgomrXQAImYwVJllOQCinLwWgazxfk7ahw820g0jApAVHSDOYxYB6TzGNEQQ7RJhtJJMFOJ8A1THYN8y_XKMgrtKhBmosW62gu7LrrzIcvn81CzQC-AZp5Qd5kobgC5Xmb2xJgVsiMNZHcn0jCOvTT4dFxTH4PdIZDOAZzA5mCvNwM45VY29aG1RpllNRIjFcX5PHgZ5t5cw0hsVJwtZp44EQx05H2cplYwjGQxgC3IO9GZ_39XH_X_NP_ofln5E6ZFht-A98nO_33dXgOwVvvXqR1eg3PfEFd priority: 102 providerName: Directory of Open Access Journals – databaseName: Health & Medical Collection dbid: 7X7 link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwfV3NbtQwELagSAgOCAqUQEEGIXEAq904duwTWhBVQSoHoNLerNixdyuhpDTZO6_B6_EkzDjOblMElxziyY9nxp4Zz_gzIS8jplpRclZU0rICfGZmVS2YFDNXCu194XE38slneXxafFqIRVpw61JZ5Tgnxom6bh2ukR9wMGxgmg558fb8B8NTozC7mo7QuE5uROgy0OdysQ24EIZ33Cij5EGHL1AMrBIE0LkUTEyMUcTs_3tmvmSarpZNXsmdRpN0dJfcSb4knQ_Cv0eu-WaX3L6EMLhLbp6k3Pl9spr3_VDbyHyziol_Wp99B1-zplh7nnSQghdLcS903Bb2--evjqYkDq19Hwu3GorV8kvao51zcAEdpkCc0K8fkNOjD9_eH7N0zgJzQvOe1UE7YAy3TgYw6VrqgFY-hJq7QlRS-5nLgygrrpyXVVlXVvFZEWZWCQvxEH9Idpq28Y8IBXdElbaGMEyGAkItqyAgUhWXhdJ4PyOvR4ab8wFOw8QwREkziMeAeEwUjxEZeYcy2VAiFHa80V4sTRpZhtehElbZ3AX4irQ6Dxb-PndOHMoqwEteoEQNgl00WE2zrNZdZz5-_WLmCtQJDTTPyKtEFFpgnqvS5gToFeJjTSj3J5QwGt20eVQck2aDzmx1NyPPN834JFa4Nb5dI42SGuHxyozsDXq26TfX4BgrBU-riQZOGDNtac5WESsc3Wl0czPyZlTW7X_9m_OP_9-NJ-RWHocRrnHvk53-Yu2fgnPW22dxBP4Bq2U3-Q priority: 102 providerName: ProQuest – databaseName: Springer Nature OA Free Journals dbid: C6C link: http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3NbtQwELagSAgOFZS_lIIMQuIAVps4duzjUlEVpHIAKvVm2Y7drYSyiGTvvAav1yfpjONdNgUOXPYQj7Px_GRmdma-JeRVwlSrG85qKx2rIWZmTrWCSVH6RugQ6oDTyCef5PFp_fFMnGWYHJyF2azfl0ru9-iPFANPAklvJQUTN8ktUXKZCrPycJ1cIeTuaijmr_smjifh8__5Ft5wQ9dbJK_VSZP7ObpHtnPcSGejoO-TG6HbIXc30AR3yO2TXCd_QOazYRj7GFno5qnIT9uLbxBXthT7zLO-UYhYKc49pxGwy5-_epoLNrQNQ2rS6ih2xp_TAX2ahw_QVwrEGen6ITk9ev_18Jjl_1RgXmg-sDZqD4zhzssI7ltLHdGjx9hyXwsrdSh9FUVjufJB2qa1TvGyjqVTwkHuwx-RrW7RhSeEQuihGtdCyiVjDWmVU5D8KMtlrTReL8ibFcPN9xE6w6SUQ0kziseAeEwSjxEFeYcyWVMi7HW6ANpgshUZ3kYrnHKVj_At0ukqOnj6yntxIG2Em7xEiRoEtuiwc-bcLvvefPjy2czUAThscMa8IK8zUVwA87zNgwhwKsTCmlDuTSjB8vx0eaU4Jlt-bzgEYHA2oCnIi_Uy7sRuti4slkijpEYovKYgj0c9W5-bawiClYLdaqKBE8ZMV7qLecIFx9AZQ9qCvF0p6-_n-jfnd_-P_Cm5UyWzwt-398jW8GMZnkFgNrjnySKvAOJsMOk priority: 102 providerName: Springer Nature |
| Title | Attention-enhanced dilated convolution for Parkinson’s disease detection using transcranial sonography |
| URI | https://link.springer.com/article/10.1186/s12938-024-01265-5 https://www.ncbi.nlm.nih.gov/pubmed/39085884 https://www.proquest.com/docview/3091293034 https://www.proquest.com/docview/3086955187 https://pubmed.ncbi.nlm.nih.gov/PMC11290250 https://doaj.org/article/3dfa5b8b2cf6486b92fb6132cc506af5 |
| Volume | 23 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1Lj9MwELb2ISE4IFhegaUyCIkDBLZx7NgHhLrVlgWpK7RQqTcrdux2pVUKbSrBjb_B3-OXMOMkZbMsBy6RGo_beB79ZuKZMSHPQk-1NGNxmgsTp-Azx0YWPBa8bzOunEsdViOPT8TxJP0w5dMt0h531DBwdWVoh-dJTZbnr759_f4WDP5NMHgpXq8Qs2QMaAOBcSJ4zLfJLiCTwGBsnG52FTB6Vm3hzJXzOuAUevj__U99Aaoup1Fe2ksNEDW6RW42viUd1Mpwm2y5co_cuNBxcI9cGzd76XfIfFBVda5j7Mp5SASgxdk5-J4FxVz0RicpeLUUa6NDmdivHz9XtNnUoYWrQiJXSTF7fkYrxD0LF9BpCsRNN-y7ZDI6-jw8jptzF2LLFaviwisLjGHGCg8Qr4TyiPreF8ymPBfK9W3ieZYzaZ3IsyI3kvVT3zeSG4iP2D2yUy5K94BQcE9kZgoIy4RPIfQyEgIkmTORSoX3I_KiZbj-UrfX0CEskULX4tEgHh3Eo3lEDlEmG0psjR1uLJYz3ViaZoXPuZEmsR5-RRiVeANPn1jLD0Tu4UueokQ1Nr8oMbtmlq9XK_3-06keyAMAdQBsFpHnDZFfAPNs3hQrwKqwX1aHcr9DCdZpu8Ot4uhWuTUDJw3WBjQRebIZxpmY8Va6xRpppFDYLi-LyP1azzbrZgocZSlhtuxoYIcx3ZHybB56h6N7jW5vRF62yvrnuf7N-Yf_R_6IXE-CWeE78H2yUy3X7jE4b5Xpke1smsFVjt71yO7h0cnHU_g0FMNeeB3SCxb7GxZoRLE |
| linkProvider | Scholars Portal |
| linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV3NbtQwELbKVuLngKD8BQoYBOIAUbtx7HUOFdpCqy7trlBppd5M7Ni7lVC2NFkhbrwGL8PD8CTMeJ1tUwS3XvYQT7KxZ8bfTDw_hLzwNdXSHovTXOg4BZs51rLgseBd0-OZtanFbOThSOwcph-O-NES-dXkwmBYZbMn-o26mBr8Rr7GANgAmtZZ-vbka4xdo_B0tWmhkYfWCsWGLzEWEjt27fdv4MJVG4P3wO-XSbK9dfBuJw5dBmLDM1bHhctMBn6aNsIBoGUic4hxzhXMpDwXme2axPFezqSxIu8VuZasm7qullyDN8DguVfIcooZrh2yvLk1-ri_cPmwEHCTqiPFWoVTkDHgIrjwieAxb8Gh7xrwNzacA8eLgZsXTm89KG7fIjeDNUv7c_G7TZZsuUJunKtxuEKuDsPp_R0y6df1PLoytuXEhx7Q4vgLWLsFxej3oAUU7GiK2dg-Me33j58VDcdItLC1Dx0rKcbrj2mNSGvgB7SIAnGov32XHF4KD-6RTjkt7QNCwSCSPV2AIyhcCs6eluCSyZyJVGZ4PSKvmwVXJ_OCHso7QlKoOXsUsEd59igekU3kyYISi3H7C9PTsQq6rVjhcq6lToyDfxE6S5yGt0-M4esid_CQ58hRheU2SoznGeezqlKDT_uqL0Gg0URgEXkViNwUFs_kIT0CZoUVulqUqy1K2A9Me7gRHBX2o0qdaU9Eni2G8U6MsSvtdIY0UmSoPb2I3J_L2WLeLAPTXEq4W7YksLUw7ZHyeOKrlaNBj4Z2RN40wnr2Xv9e-Yf_n8ZTcm3nYLin9gaj3UfkeuJVCr-4r5JOfTqzj8FUrPWToI-UfL7sLeAPaeZ7vQ |
| linkToPdf | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3NbtQwELagSBUcKih_KQUMQuIAVrtx7NjHpbBqgVYIqNSbFTv2biWUrZrsndfg9XgSZhzvsilw4LKHeLwbe8b5ZjIz3xLyInKqFSVnRSUtK8BnZlbVgkkxcqXQ3hceu5GPT-ThafH-TJytdfHHavdlSrLvaUCWpqbbu6hDf8SV3GsRpRQDfIFQOJeCievkRoHQh-laebAKuZCId9kq89d5AziKrP1_PpvXwOlq4eSV7GkEpcltspW8STru1X-HXPPNNrm1xjG4TTaPU_b8LpmNu66vbmS-mcXUP63Pv4G3WVOsPk9WSMGPpdgNHRvDfn7_0dKUxqG172LpVkOxXn5KO0Q6Bx9gxRSEE__1PXI6eff14JClf1pgTmjesTpoBxvDrZMBQF1LHRDnQ6i5K0QltR-5PIiy4sp5WZV1ZRUfFWFklbAQEfH7ZKOZN_4hoeCQqNLWEIjJUECwZRWERKrislAar2fk1XLDzUVPqGFiIKKk6dVjQD0mqseIjLxBnawkkQw7XphfTk06W4bXoRJW2dwF-BVpdR4s3H3unNiXVYAveY4aNUh30WA9zbRatK05-vLZjNU-wDhANM_IyyQU5rB5rkrtCbAqZMgaSO4OJOE8uuHw0nBMeh60hoNtwtpAJiPPVsM4E2vcGj9foIySGgnyyow86O1stW6uwTVWCmargQUONmY40pzPIls4OtTo6Gbk9dJYf9_Xv3d-5__En5LNT28n5uPRyYdH5GYeTxi-AN8lG93lwj8Gz62zT-Lh_AWmdTwd |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Attention-enhanced+dilated+convolution+for+Parkinson%E2%80%99s+disease+detection+using+transcranial+sonography&rft.jtitle=Biomedical+engineering+online&rft.au=Chen%2C+Shuang&rft.au=Shi%2C+Yuting&rft.au=Wan%2C+Linlin&rft.au=Liu%2C+Jing&rft.date=2024-07-31&rft.pub=BioMed+Central&rft.eissn=1475-925X&rft.volume=23&rft.issue=1&rft_id=info:doi/10.1186%2Fs12938-024-01265-5&rft.externalDocID=10_1186_s12938_024_01265_5 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1475-925X&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1475-925X&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1475-925X&client=summon |