Medical image analysis based on deep learning approach

Medical imaging plays a significant role in different clinical applications such as medical procedures used for early detection, monitoring, diagnosis, and treatment evaluation of various medical conditions. Basicsof the principles and implementations of artificial neural networks and deep learning...

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Published inMultimedia tools and applications Vol. 80; no. 16; pp. 24365 - 24398
Main Authors Puttagunta, Muralikrishna, Ravi, S.
Format Journal Article
LanguageEnglish
Published New York Springer US 01.07.2021
Springer Nature B.V
Subjects
Artificial neural networks
Computed tomography
Computer Communication Networks
Computer Science
Computer vision
Data Structures and Information Theory
Deep learning
Digital imaging
Image analysis
Image classification
Image segmentation
Machine learning
Medical imaging
Medical research
Multimedia Information Systems
Neural networks
Special Purpose and Application-Based Systems
Deep learning
Medical images
Segmentation
Convolutional neural networks
Detection
Classification
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Abstract Medical imaging plays a significant role in different clinical applications such as medical procedures used for early detection, monitoring, diagnosis, and treatment evaluation of various medical conditions. Basicsof the principles and implementations of artificial neural networks and deep learning are essential for understanding medical image analysis in computer vision. Deep Learning Approach (DLA) in medical image analysis emerges as a fast-growing research field. DLA has been widely used in medical imaging to detect the presence or absence of the disease. This paper presents the development of artificial neural networks, comprehensive analysis of DLA, which delivers promising medical imaging applications. Most of the DLA implementations concentrate on the X-ray images, computerized tomography, mammography images, and digital histopathology images. It provides a systematic review of the articles for classification, detection, and segmentation of medical images based on DLA. This review guides the researchers to think of appropriate changes in medical image analysis based on DLA.
AbstractList Medical imaging plays a significant role in different clinical applications such as medical procedures used for early detection, monitoring, diagnosis, and treatment evaluation of various medical conditions. Basicsof the principles and implementations of artificial neural networks and deep learning are essential for understanding medical image analysis in computer vision. Deep Learning Approach (DLA) in medical image analysis emerges as a fast-growing research field. DLA has been widely used in medical imaging to detect the presence or absence of the disease. This paper presents the development of artificial neural networks, comprehensive analysis of DLA, which delivers promising medical imaging applications. Most of the DLA implementations concentrate on the X-ray images, computerized tomography, mammography images, and digital histopathology images. It provides a systematic review of the articles for classification, detection, and segmentation of medical images based on DLA. This review guides the researchers to think of appropriate changes in medical image analysis based on DLA.
Medical imaging plays a significant role in different clinical applications such as medical procedures used for early detection, monitoring, diagnosis, and treatment evaluation of various medical conditions. Basicsof the principles and implementations of artificial neural networks and deep learning are essential for understanding medical image analysis in computer vision. Deep Learning Approach (DLA) in medical image analysis emerges as a fast-growing research field. DLA has been widely used in medical imaging to detect the presence or absence of the disease. This paper presents the development of artificial neural networks, comprehensive analysis of DLA, which delivers promising medical imaging applications. Most of the DLA implementations concentrate on the X-ray images, computerized tomography, mammography images, and digital histopathology images. It provides a systematic review of the articles for classification, detection, and segmentation of medical images based on DLA. This review guides the researchers to think of appropriate changes in medical image analysis based on DLA.Medical imaging plays a significant role in different clinical applications such as medical procedures used for early detection, monitoring, diagnosis, and treatment evaluation of various medical conditions. Basicsof the principles and implementations of artificial neural networks and deep learning are essential for understanding medical image analysis in computer vision. Deep Learning Approach (DLA) in medical image analysis emerges as a fast-growing research field. DLA has been widely used in medical imaging to detect the presence or absence of the disease. This paper presents the development of artificial neural networks, comprehensive analysis of DLA, which delivers promising medical imaging applications. Most of the DLA implementations concentrate on the X-ray images, computerized tomography, mammography images, and digital histopathology images. It provides a systematic review of the articles for classification, detection, and segmentation of medical images based on DLA. This review guides the researchers to think of appropriate changes in medical image analysis based on DLA.
Author Puttagunta, Muralikrishna
Ravi, S.
Author_xml – sequence: 1
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  surname: Puttagunta
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  surname: Ravi
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  organization: Department of Computer Science, School of Engineering and Technology, Pondicherry University
BackLink https://www.ncbi.nlm.nih.gov/pubmed/33841033$$D View this record in MEDLINE/PubMed
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IsDoiOpenAccess true
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Issue 16
Keywords Deep learning
Medical images
Segmentation
Convolutional neural networks
Detection
Classification
Language English
License The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021.
This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
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  year: 2018
  ident: 10707_CR46
  publication-title: IEEE Trans Image Process
  doi: 10.1109/TIP.2018.2801119
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Snippet Medical imaging plays a significant role in different clinical applications such as medical procedures used for early detection, monitoring, diagnosis, and...
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SubjectTerms Artificial neural networks
Computed tomography
Computer Communication Networks
Computer Science
Computer vision
Data Structures and Information Theory
Deep learning
Digital imaging
Image analysis
Image classification
Image segmentation
Machine learning
Medical imaging
Medical research
Multimedia Information Systems
Neural networks
Special Purpose and Application-Based Systems
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Title Medical image analysis based on deep learning approach
URI https://link.springer.com/article/10.1007/s11042-021-10707-4
https://www.ncbi.nlm.nih.gov/pubmed/33841033
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