Deep ocular tumor classification model using cuckoo search algorithm and Caputo fractional gradient descent
While digital ocular fundus images are commonly used for diagnosing ocular tumors, interpreting these images poses challenges due to their complexity and the subtle features specific to tumors. Automated detection of ocular tumors is crucial for timely diagnosis and effective treatment. This study i...
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Published in | PeerJ. Computer science Vol. 10; p. e1923 |
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Format | Journal Article |
Language | English |
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Abstract | While digital ocular fundus images are commonly used for diagnosing ocular tumors, interpreting these images poses challenges due to their complexity and the subtle features specific to tumors. Automated detection of ocular tumors is crucial for timely diagnosis and effective treatment. This study investigates a robust deep learning system designed for classifying ocular tumors. The article introduces a novel optimizer that integrates the Caputo fractional gradient descent (CFGD) method with the cuckoo search algorithm (CSA) to enhance accuracy and convergence speed, seeking optimal solutions. The proposed optimizer’s performance is assessed by training well-known Vgg16, AlexNet, and GoogLeNet models on 400 fundus images, equally divided between benign and malignant classes. Results demonstrate the significant potential of the proposed optimizer in improving classification accuracy and convergence speed. In particular, the mean accuracy attained by the proposed optimizer is 86.43%, 87.42%, and 87.62% for the Vgg16, AlexNet, and GoogLeNet models, respectively. The performance of our optimizer is compared with existing approaches, namely stochastic gradient descent with momentum (SGDM), adaptive momentum estimation (ADAM), the original cuckoo search algorithm (CSA), Caputo fractional gradient descent (CFGD), beetle antenna search with ADAM (BASADAM), and CSA with ADAM (CSA-ADAM). Evaluation criteria encompass accuracy, robustness, consistency, and convergence speed. Comparative results highlight significant enhancements across all metrics, showcasing the potential of deep learning techniques with the proposed optimizer for accurately identifying ocular tumors. This research contributes significantly to the development of computer-aided diagnosis systems for ocular tumors, emphasizing the benefits of the proposed optimizer in medical image classification domains. |
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AbstractList | While digital ocular fundus images are commonly used for diagnosing ocular tumors, interpreting these images poses challenges due to their complexity and the subtle features specific to tumors. Automated detection of ocular tumors is crucial for timely diagnosis and effective treatment. This study investigates a robust deep learning system designed for classifying ocular tumors. The article introduces a novel optimizer that integrates the Caputo fractional gradient descent (CFGD) method with the cuckoo search algorithm (CSA) to enhance accuracy and convergence speed, seeking optimal solutions. The proposed optimizer’s performance is assessed by training well-known Vgg16, AlexNet, and GoogLeNet models on 400 fundus images, equally divided between benign and malignant classes. Results demonstrate the significant potential of the proposed optimizer in improving classification accuracy and convergence speed. In particular, the mean accuracy attained by the proposed optimizer is 86.43%, 87.42%, and 87.62% for the Vgg16, AlexNet, and GoogLeNet models, respectively. The performance of our optimizer is compared with existing approaches, namely stochastic gradient descent with momentum (SGDM), adaptive momentum estimation (ADAM), the original cuckoo search algorithm (CSA), Caputo fractional gradient descent (CFGD), beetle antenna search with ADAM (BASADAM), and CSA with ADAM (CSA-ADAM). Evaluation criteria encompass accuracy, robustness, consistency, and convergence speed. Comparative results highlight significant enhancements across all metrics, showcasing the potential of deep learning techniques with the proposed optimizer for accurately identifying ocular tumors. This research contributes significantly to the development of computer-aided diagnosis systems for ocular tumors, emphasizing the benefits of the proposed optimizer in medical image classification domains. |
ArticleNumber | e1923 |
Author | Ahmed Ali Ali, Talal Taresh, Mundher Mohammed Habeb, Abduljlil Abduljlil Ali Abduljlil Zhu, Ningbo |
Author_xml | – sequence: 1 givenname: Abduljlil Abduljlil Ali Abduljlil surname: Habeb fullname: Habeb, Abduljlil Abduljlil Ali Abduljlil organization: College of Computer Science and Electronic Engineering, Hunan University, Changsha, Hunan, China – sequence: 2 givenname: Ningbo surname: Zhu fullname: Zhu, Ningbo organization: College of Computer Science and Electronic Engineering, Hunan University, Changsha, Hunan, China, Research Institute, Hunan University, Chongqing, Chongqing, China – sequence: 3 givenname: Mundher Mohammed surname: Taresh fullname: Taresh, Mundher Mohammed organization: College of Computer Science and Electronic Engineering, Hunan University, Changsha, Hunan, China – sequence: 4 givenname: Talal surname: Ahmed Ali Ali fullname: Ahmed Ali Ali, Talal organization: College of Computer Science and Electronic Engineering, Hunan University, Changsha, Hunan, China |
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References | Kaliki (10.7717/peerj-cs.1923/ref-18) 2023; 71 Mohsin (10.7717/peerj-cs.1923/ref-23) 2020; 8 Salma (10.7717/peerj-cs.1923/ref-34) 2021 Montgomery (10.7717/peerj-cs.1923/ref-24) 2010 Moothedath (10.7717/peerj-cs.1923/ref-25) 2023 Russakovsky (10.7717/peerj-cs.1923/ref-33) 2015; 115 Szegedy (10.7717/peerj-cs.1923/ref-41) 2015 Chen (10.7717/peerj-cs.1923/ref-6) 2019 Kumar (10.7717/peerj-cs.1923/ref-20) 2023; 2023 Pogosova (10.7717/peerj-cs.1923/ref-30) 2022; 10 Bilal (10.7717/peerj-cs.1923/ref-5) 2022; 22 Gogna (10.7717/peerj-cs.1923/ref-10) 2013; 25 Atwany (10.7717/peerj-cs.1923/ref-4) 2022; 10 Honavar (10.7717/peerj-cs.1923/ref-15) 2021; 69 Neupane (10.7717/peerj-cs.1923/ref-28) 2018; 20 Shin (10.7717/peerj-cs.1923/ref-38) 2021 Grishina (10.7717/peerj-cs.1923/ref-12) 2023; 23 Goel (10.7717/peerj-cs.1923/ref-9) 2015; 57 Ólafsson (10.7717/peerj-cs.1923/ref-29) 2006; 13 Goswami (10.7717/peerj-cs.1923/ref-11) 2021; 93 Nawaz (10.7717/peerj-cs.1923/ref-26) 2022; 22 Nesmachnow (10.7717/peerj-cs.1923/ref-27) 2014; 3 Sengupta (10.7717/peerj-cs.1923/ref-35) 2018 Manjandavida (10.7717/peerj-cs.1923/ref-22) 2019; 67 Kadry (10.7717/peerj-cs.1923/ref-17) 2022; 78 Jin (10.7717/peerj-cs.1923/ref-16) 2022; 100 Simonyan (10.7717/peerj-cs.1923/ref-40) 2014 Wang (10.7717/peerj-cs.1923/ref-44) 2017; 89 American Society of Retina Specialists (10.7717/peerj-cs.1923/ref-1) 2022 Guerrout (10.7717/peerj-cs.1923/ref-13) 2020 Liu (10.7717/peerj-cs.1923/ref-21) 2019; 22 Taresh (10.7717/peerj-cs.1923/ref-42) 2022 Velpula (10.7717/peerj-cs.1923/ref-43) 2023; 14 Gündüz (10.7717/peerj-cs.1923/ref-14) 2023 Ramírez-Ortiz (10.7717/peerj-cs.1923/ref-32) 2017; 74 Akter (10.7717/peerj-cs.1923/ref-2) 2022; 12 Shin (10.7717/peerj-cs.1923/ref-39) 2023; 161 Deepika (10.7717/peerj-cs.1923/ref-8) 2021; 2 Albashish (10.7717/peerj-cs.1923/ref-3) 2021 Das (10.7717/peerj-cs.1923/ref-7) 2022; 81 Pu (10.7717/peerj-cs.1923/ref-31) 2013; 26 Shaik (10.7717/peerj-cs.1923/ref-36) 2022; 52 Sheng (10.7717/peerj-cs.1923/ref-37) 2020; 408 Khan (10.7717/peerj-cs.1923/ref-19) 2020; 7 |
References_xml | – volume: 7 start-page: 461 issue: 2 year: 2020 ident: 10.7717/peerj-cs.1923/ref-19 article-title: BAS-ADAM: an ADAM based approach to improve the performance of beetle antennae search optimizer publication-title: IEEE/CAA Journal of Automatica Sinica doi: 10.1109/JAS.2020.1003048 contributor: fullname: Khan – volume: 3 start-page: 320 issue: 4 year: 2014 ident: 10.7717/peerj-cs.1923/ref-27 article-title: An overview of metaheuristics: accurate and efficient methods for optimisation publication-title: International Journal of Metaheuristics doi: 10.1504/IJMHEUR.2014.068914 contributor: fullname: Nesmachnow – volume: 74 start-page: 41 issue: 1 year: 2017 ident: 10.7717/peerj-cs.1923/ref-32 article-title: Systematic review of the current status of programs and general knowledge of diagnosis and management of retinoblastoma publication-title: Boletín Médico Del Hospital Infantil de México (English Edition) doi: 10.1016/j.bmhime.2017.12.001 contributor: fullname: Ramírez-Ortiz – start-page: 1 year: 2021 ident: 10.7717/peerj-cs.1923/ref-34 article-title: Diabetic retinopathy detection using GoogleNet architecture of convolutional neural network through fundus images publication-title: Nusantara Science and Technology Proceedings contributor: fullname: Salma – year: 2018 ident: 10.7717/peerj-cs.1923/ref-35 article-title: Application of deep learning in fundus image processing for ophthalmic diagnosis—a review contributor: fullname: Sengupta – start-page: 546 year: 2019 ident: 10.7717/peerj-cs.1923/ref-6 article-title: A Caputo-type fractional-order gradient descent learning of deep BP neural networks contributor: fullname: Chen – volume: 57 start-page: 377 year: 2015 ident: 10.7717/peerj-cs.1923/ref-9 article-title: Nature inspired algorithms in remote sensing image classification publication-title: Procedia Computer Science doi: 10.1016/j.procs.2015.07.352 contributor: fullname: Goel – volume: 71 start-page: 424 issue: 2 year: 2023 ident: 10.7717/peerj-cs.1923/ref-18 article-title: Artificial intelligence and machine learning in ocular oncology: retinoblastoma publication-title: Indian Journal of Ophthalmology doi: 10.4103/ijo.IJO_1393_22 contributor: fullname: Kaliki – volume: 22 start-page: 653 issue: 10 year: 2019 ident: 10.7717/peerj-cs.1923/ref-21 article-title: Clinical diagnosis and treatment recommendations for ocular toxicities of target therapy and immune checkpoint inhibitor therapy publication-title: Zhongguo Fei Ai Za Zhi = Chinese Journal of Lung Cancer contributor: fullname: Liu – volume-title: Applied statistics and probability for engineers year: 2010 ident: 10.7717/peerj-cs.1923/ref-24 contributor: fullname: Montgomery – volume: 2023 year: 2023 ident: 10.7717/peerj-cs.1923/ref-20 article-title: A multi-thresholding-based discriminative neural classifier for detection of retinoblastoma using CNN models publication-title: BioMed Research International doi: 10.1155/2023/5803661 contributor: fullname: Kumar – volume: 93 start-page: 101986 year: 2021 ident: 10.7717/peerj-cs.1923/ref-11 article-title: Deep learning models for benign and malign ocular tumor growth estimation publication-title: Computerized Medical Imaging and Graphics doi: 10.1016/j.compmedimag.2021.101986 contributor: fullname: Goswami – start-page: 1 year: 2015 ident: 10.7717/peerj-cs.1923/ref-41 article-title: Going deeper with convolutions contributor: fullname: Szegedy – year: 2021 ident: 10.7717/peerj-cs.1923/ref-38 article-title: A caputo fractional derivative-based algorithm for optimization contributor: fullname: Shin – start-page: 11206721231163619 year: 2023 ident: 10.7717/peerj-cs.1923/ref-14 article-title: Follow-up of retinoblastoma using RetCam fluorescein angiography and correlation with clinical findings publication-title: European Journal of Ophthalmology contributor: fullname: Gündüz – volume: 69 start-page: 1979 issue: 8 year: 2021 ident: 10.7717/peerj-cs.1923/ref-15 article-title: The red reflex test-shadow conceals, light reveals publication-title: Indian Journal of Ophthalmology doi: 10.4103/ijo.IJO_1917_21 contributor: fullname: Honavar – start-page: 133 volume-title: Global perspectives in ocular oncology year: 2023 ident: 10.7717/peerj-cs.1923/ref-25 article-title: Orbital and metastatic retinoblastoma: conquests and challenges contributor: fullname: Moothedath – volume: 408 start-page: 42 year: 2020 ident: 10.7717/peerj-cs.1923/ref-37 article-title: Convolutional neural networks with fractional order gradient method publication-title: Neurocomputing doi: 10.1016/j.neucom.2019.10.017 contributor: fullname: Sheng – volume: 10 start-page: 28642 year: 2022 ident: 10.7717/peerj-cs.1923/ref-4 article-title: Deep learning techniques for diabetic retinopathy classification: a survey publication-title: IEEE Access doi: 10.1109/ACCESS.2022.3157632 contributor: fullname: Atwany – volume: 14 start-page: 1175881 year: 2023 ident: 10.7717/peerj-cs.1923/ref-43 article-title: Multi-stage glaucoma classification using pre-trained convolutional neural networks and voting-based classifier fusion publication-title: Frontiers in Physiology doi: 10.3389/fphys.2023.1175881 contributor: fullname: Velpula – volume: 25 start-page: 503 issue: 4 year: 2013 ident: 10.7717/peerj-cs.1923/ref-10 article-title: Metaheuristics: review and application publication-title: Journal of Experimental & Theoretical Artificial Intelligence doi: 10.1080/0952813X.2013.782347 contributor: fullname: Gogna – volume: 20 start-page: 1 year: 2018 ident: 10.7717/peerj-cs.1923/ref-28 article-title: Imaging techniques in the diagnosis and management of ocular tumors: prospects and challenges publication-title: The AAPS Journal doi: 10.1208/s12248-017-0160-y contributor: fullname: Neupane – year: 2022 ident: 10.7717/peerj-cs.1923/ref-1 article-title: Retina image bank: a project from the American Society of Retina Specialists contributor: fullname: American Society of Retina Specialists – volume: 161 start-page: 185 year: 2023 ident: 10.7717/peerj-cs.1923/ref-39 article-title: Accelerating gradient descent and Adam via fractional gradients publication-title: Neural Networks doi: 10.1016/j.neunet.2023.01.002 contributor: fullname: Shin – volume: 10 start-page: e1076–e1077 issue: 8 year: 2022 ident: 10.7717/peerj-cs.1923/ref-30 article-title: May measurement month: beyond boosting hypertension awareness publication-title: The Lancet Global Health doi: 10.1016/S2214-109X(22)00270-4 contributor: fullname: Pogosova – volume: 2 start-page: 263 issue: 4 year: 2021 ident: 10.7717/peerj-cs.1923/ref-8 article-title: A robust deep features enabled touchless 3D-fingerprint classification system publication-title: SN Computer Science doi: 10.1007/s42979-021-00657-x contributor: fullname: Deepika – year: 2014 ident: 10.7717/peerj-cs.1923/ref-40 article-title: Very deep convolutional networks for large-scale image recognition contributor: fullname: Simonyan – volume: 67 start-page: 958 issue: 6 year: 2019 ident: 10.7717/peerj-cs.1923/ref-22 article-title: In-utero ultrasonography detection of fetal retinoblastoma and neonatal selective ophthalmic artery chemotherapy publication-title: Indian Journal of Ophthalmology doi: 10.4103/ijo.IJO_340_19 contributor: fullname: Manjandavida – volume: 89 start-page: 19 year: 2017 ident: 10.7717/peerj-cs.1923/ref-44 article-title: Fractional-order gradient descent learning of BP neural networks with Caputo derivative publication-title: Neural Networks doi: 10.1016/j.neunet.2017.02.007 contributor: fullname: Wang – start-page: 805 year: 2021 ident: 10.7717/peerj-cs.1923/ref-3 article-title: Deep CNN model based on VGG16 for breast cancer classification contributor: fullname: Albashish – volume: 23 start-page: 107 issue: 2 year: 2023 ident: 10.7717/peerj-cs.1923/ref-12 article-title: Episcleral spread of ciliochoroidal melanoma following surgeries: a case report publication-title: Russian Journal of Clinical Ophthalmology doi: 10.32364/2311-7729-2023-23-2-107-110 contributor: fullname: Grishina – volume: 81 start-page: 25613 issue: 18 year: 2022 ident: 10.7717/peerj-cs.1923/ref-7 article-title: A critical review on diagnosis of diabetic retinopathy using machine learning and deep learning publication-title: Multimedia Tools and Applications doi: 10.1007/s11042-022-12642-4 contributor: fullname: Das – volume: 78 start-page: 7321 issue: 5 year: 2022 ident: 10.7717/peerj-cs.1923/ref-17 article-title: Automated detection of age-related macular degeneration using a pre-trained deep-learning scheme publication-title: The Journal of Supercomputing doi: 10.1007/s11227-021-04181-w contributor: fullname: Kadry – volume: 22 start-page: 9603 issue: 24 year: 2022 ident: 10.7717/peerj-cs.1923/ref-5 article-title: IGWO-IVNet3: DL-based automatic diagnosis of lung nodules using an improved gray wolf optimization and InceptionNet-V3 publication-title: Sensors doi: 10.3390/s22249603 contributor: fullname: Bilal – volume: 100 start-page: e512–e520 issue: 2 year: 2022 ident: 10.7717/peerj-cs.1923/ref-16 article-title: Multimodal deep learning with feature level fusion for identification of choroidal neovascularization activity in age-related macular degeneration publication-title: Acta Ophthalmologica doi: 10.1111/aos.14928 contributor: fullname: Jin – volume: 8 start-page: 105542 year: 2020 ident: 10.7717/peerj-cs.1923/ref-23 article-title: Optimization driven adam-cuckoo search-based deep belief network classifier for data classification publication-title: IEEE Access doi: 10.1109/ACCESS.2020.2999865 contributor: fullname: Mohsin – volume: 52 start-page: 15105 issue: 13 year: 2022 ident: 10.7717/peerj-cs.1923/ref-36 article-title: Hinge attention network: a joint model for diabetic retinopathy severity grading publication-title: Applied Intelligence doi: 10.1007/s10489-021-03043-5 contributor: fullname: Shaik – volume: 12 start-page: 8064 issue: 1 year: 2022 ident: 10.7717/peerj-cs.1923/ref-2 article-title: Glaucoma diagnosis using multi-feature analysis and a deep learning technique publication-title: Scientific Reports doi: 10.1038/s41598-022-12147-y contributor: fullname: Akter – year: 2022 ident: 10.7717/peerj-cs.1923/ref-42 article-title: Using a novel fractional-order gradient method for CNN back-propagation contributor: fullname: Taresh – year: 2020 ident: 10.7717/peerj-cs.1923/ref-13 article-title: hidden markov random fields and cuckoo search method for medical image segmentation contributor: fullname: Guerrout – volume: 22 start-page: 434 issue: 2 year: 2022 ident: 10.7717/peerj-cs.1923/ref-26 article-title: An efficient deep learning approach to automatic glaucoma detection using optic disc and optic cup localization publication-title: Sensors doi: 10.3390/s22020434 contributor: fullname: Nawaz – volume: 13 start-page: 633 year: 2006 ident: 10.7717/peerj-cs.1923/ref-29 article-title: Metaheuristics publication-title: Handbooks in Operations Research and Management Science doi: 10.1016/S0927-0507(06)13021-2 contributor: fullname: Ólafsson – volume: 26 start-page: 653 issue: 4 year: 2013 ident: 10.7717/peerj-cs.1923/ref-31 article-title: Fractional extreme value adaptive training method: fractional steepest descent approach publication-title: IEEE Transactions on Neural Networks and Learning Systems doi: 10.1109/TNNLS.2013.2286175 contributor: fullname: Pu – volume: 115 start-page: 211 year: 2015 ident: 10.7717/peerj-cs.1923/ref-33 article-title: Imagenet large scale visual recognition challenge publication-title: International Journal of Computer Vision doi: 10.1007/s11263-015-0816-y contributor: fullname: Russakovsky |
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