{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,23]],"date-time":"2026-07-23T01:57:02Z","timestamp":1784771822992,"version":"3.55.0"},"reference-count":100,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2025,8,25]],"date-time":"2025-08-25T00:00:00Z","timestamp":1756080000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Bioengineering"],"abstract":"<jats:p>Optical coherence tomography (OCT) is a leading imaging technique for diagnosing retinal disorders such as age-related macular degeneration and diabetic retinopathy. Its ability to detect structural changes, especially in the optic nerve head, has made it vital for early diagnosis and monitoring. This paper surveys techniques for ocular disease prediction using OCT, focusing on both hand-crafted and deep learning-based feature extractors. While the field has seen rapid growth, a detailed comparative analysis of these methods has been lacking. We address this by reviewing research from the past 20 years, evaluating methods based on accuracy, sensitivity, specificity, and computational cost. Key diseases examined include glaucoma, diabetic retinopathy, cataracts, amblyopia, and macular degeneration. We also assess public OCT datasets widely used in model development. A unique contribution of this paper is the exploration of adversarial attacks targeting OCT-based diagnostic systems and the vulnerabilities of different feature extraction techniques. We propose a practical, robust defense strategy that integrates with existing models and outperforms current solutions. Our findings emphasize the value of combining classical and deep learning methods with strong defenses to enhance the security and reliability of OCT-based diagnostics, and we offer guidance for future research and clinical integration.<\/jats:p>","DOI":"10.3390\/bioengineering12090914","type":"journal-article","created":{"date-parts":[[2025,8,26]],"date-time":"2025-08-26T06:26:31Z","timestamp":1756189591000},"page":"914","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Comprehensive Survey of OCT-Based Disorders Diagnosis: From Feature Extraction Methods to Robust Security Frameworks"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7643-2235","authenticated-orcid":false,"given":"Alex","family":"Liew","sequence":"first","affiliation":[{"name":"Graduate Center, City University of New York, New York, NY 10016, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sos","family":"Agaian","sequence":"additional","affiliation":[{"name":"College of Staten Island, City University of New York, New York, NY 10314, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,8,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1016\/j.neucom.2022.08.021","article-title":"Collins, Deep learning in retinal optical coherence tomography (OCT): A comprehensive survey","volume":"507","author":"Viedma","year":"2022","journal-title":"Neurocomputing"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"449","DOI":"10.1007\/s11831-016-9174-3","article-title":"Computer Vision Techniques Applied for Diagnostic Analysis of Retinal OCT Images: A Review","volume":"24","author":"Usman","year":"2017","journal-title":"Arch. Comput. Methods Eng."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Meiburger, K.M., Salvi, M., Rotunno, G., Drexler, W., and Liu, M. (2021). Automatic Segmentation and Classification Methods Using Optical Coherence Tomography Angiography (OCTA): A Review and Handbook. Appl. Sci., 11.","DOI":"10.3390\/app11209734"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"307","DOI":"10.1109\/RBME.2021.3110958","article-title":"Retinal OCT Image Registration: Methods and Applications","volume":"16","author":"Pan","year":"2023","journal-title":"IEEE Rev. Biomed. Eng."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Elsharkawy, M., Elrazzaz, M., Ghazal, M., Alhalabi, M., Soliman, A., Mahmoud, A., El-Daydamony, E., Atwan, A., Thanos, A., and Sandhu, H.S. (2021). Role of Optical Coherence Tomography Imaging in Predicting Progression of Age-Related Macular Disease: A Survey. Diagnostics, 11.","DOI":"10.3390\/diagnostics11122313"},{"key":"ref_6","unstructured":"Raj, J.S. (2020, January 23\u201324). A Survey on Classification and Prediction of Glaucoma and AMD Based on OCT and Fundus Images. Proceedings of the International Conference on Mobile Computing and Sustainable Informatics (ICMCSI 2020), Lalitpur, Nepal."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Kiefer, R., Steen, J., Abid, M., Ardali, M.R., and Amjadian, E. (2022, January 12\u201315). A Survey of Glaucoma Detection Algorithms using Fundus and OCT Images. Proceedings of the 2022 IEEE 13th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON), Vancouver, BC, Canada.","DOI":"10.1109\/IEMCON56893.2022.9946629"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Naveed, M., Ramzan, A., and Akram, M.U. (2017, January 19\u201321). Clinical and technical perspective of glaucoma detection using OCT and fundus images: A review. Proceedings of the 2017 1st International Conference on Next Generation Computing Applications (NextComp), Mauritius.","DOI":"10.1109\/NEXTCOMP.2017.8016192"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"188","DOI":"10.1038\/s41433-020-01191-5","article-title":"Deep learning in glaucoma with optical coherence tomography: A review","volume":"35","author":"Ran","year":"2021","journal-title":"Eye"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Akpinar, M.H., Sengur, A., Faust, O., Tong, L., Molinari, F., and Acharya, U.R. (2024). Rajendra Acharya, Artificial Intelligence in Retinal Screening Using OCT Images: A Review of the Last Decade (2013\u20132023). Comput. Methods Programs Biomed., 254.","DOI":"10.1016\/j.cmpb.2024.108253"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Nugroho, K.A. (2018, January 30\u201331). A Comparison of Handcrafted and Deep Neural Network Feature Extraction for Classifying Optical Coherence Tomography (OCT) Images. Proceedings of the 2018 2nd International Conference on Informatics and Computational Sciences (ICICoS), Semarang, Indonesia.","DOI":"10.1109\/ICICOS.2018.8621687"},{"key":"ref_12","unstructured":"Naren, O.S. (2024, January 01). Retinal OCT-C8. Available online: https:\/\/www.kaggle.com\/datasets\/obulisainaren\/retinal-oct-c8."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"171221","DOI":"10.1109\/ACCESS.2024.3496565","article-title":"Advancements in Deep Learning for Automated Diagnosis of Ophthalmic Diseases: A Comprehensive Review","volume":"12","author":"Dash","year":"2024","journal-title":"IEEE Access"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2392","DOI":"10.1109\/TMI.2021.3077484","article-title":"Deep Relation Transformer for Diagnosing Glaucoma with Optical Coherence Tomography and Visual Field Function","volume":"40","author":"Song","year":"2021","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Gholami, P., Sheikhhassani, M., Zelek, J.S., Lakshminarayanan, V., Parthasarathy, M.K., Azar, F.S., and Intes, X. (2018, January 16). Classification of optical coherence tomography images for diagnosing different ocular diseases. Proceedings of the SPIE 10487, Multimodal Biomedical Imaging XIII, Francisco, CA, USA.","DOI":"10.1117\/12.2292520"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Yu, Y.-W., Lin, C.-H., Lu, C.-K., Wang, J.-K., and Huang, T.-L. (2023). Automated Age-Related Macular Degeneration Detector on Optical Coherence Tomography Images Using Slice-Sum Local Binary Patterns and Support Vector Machine. Sensors, 23.","DOI":"10.3390\/s23177315"},{"key":"ref_17","first-page":"3298606","article-title":"Classification of SD-OCT Volumes Using Local Binary Patterns: Experimental Validation for DME Detection, Hindawi","volume":"2016","author":"Rastgoo","year":"2016","journal-title":"J. Ophthalmol."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Alsaih, K., Lemaitre, G., Vall, J.M., Rastgoo, M., Sidibe, D., Wong, T.Y., Lamoureux, E., Milea, D., Cheung, C.Y., and Meriaudeau, F. (2016, January 16\u201320). Classification of SD-OCT volumes with multi pyramids, LBP and HOG descriptors: Application to DME detections. Proceedings of the 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Orlando, FL, USA.","DOI":"10.1109\/EMBC.2016.7590956"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Liew, A. (2024, January 7). Multi-kernel Wiener local binary patterns for OCT ocular disease detections with resiliency to Gaussian noises. Proceedings of the SPIE 13033, Multimodal Image Exploitation and Learning 2024, National Harbor, MD, USA.","DOI":"10.1117\/12.3011423"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"120617","DOI":"10.1016\/j.eswa.2023.120617","article-title":"A Hybrid R-FTCNN based on principal component analysis for retinal disease detection from OCT images","volume":"230","author":"Kayadibi","year":"2023","journal-title":"Expert Syst. Appl."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Diao, S., Su, J., Yang, C., Zhu, W., Xiang, D., Chen, X., Peng, Q., and Shi, F. (2023). Classification and segmentation of OCT images for age-related macular degeneration based on dual guidance networks. Biomed. Signal Process. Control, 84.","DOI":"10.1016\/j.bspc.2023.104810"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Barua, P.D., Chan, W.Y., Dogan, S., Baygin, M., Tuncer, T., Ciaccio, E.J., Islam, N., Cheong, K.H., Shahid, Z.S., and Acharya, U.R. (2021). Multilevel Deep Feature Generation Framework for Automated Detection of Retinal Abnormalities Using OCT Images. Entropy, 23.","DOI":"10.3390\/e23121651"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Ji, Q., He, W., Huang, J., and Sun, Y. (2018). Efficient Deep Learning-Based Automated Pathology Identification in Retinal Optical Coherence Tomography Images. Algorithms, 11.","DOI":"10.3390\/a11060088"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1007\/s11517-019-02066-y","article-title":"AOCT-NET: A convolutional network automated classification of multiclass retinal diseases using spectral-domain optical coherence tomography images","volume":"58","author":"Alqudah","year":"2020","journal-title":"Med. Biol. Eng. Comput."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"327","DOI":"10.1016\/j.jvcir.2019.01.022","article-title":"Iterative fusion convolutional neural networks for classification of optical coherence tomography images","volume":"59","author":"Fang","year":"2019","journal-title":"J. Vis. Commun. Image Represent."},{"key":"ref_26","first-page":"100652","article-title":"IoT based optical coherence tomography retinal images classification using OCT Deep Net2, Measurement","volume":"25","author":"Rajan","year":"2023","journal-title":"Sensors"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Tsuji, T., Hirose, Y., Fujimori, K., Hirose, T., Oyama, A., Saikawa, Y., Mimura, T., Shiraishi, K., Kobayashi, T., and Mizota, A. (2020). Classification of optical coherence tomography images using a capsule network. BMC Ophthalmol., 20.","DOI":"10.1186\/s12886-020-01382-4"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Fu, H., Garvin, M., MacGillivray, T., Xu, Y., and Zheng, Y. (2019). Dictionary Learning Informed Deep Neural Network with Application to OCT Images. Ophthalmic Medical Image Analysis, Springer. OMIA 2019. Lecture Notes in Computer Science.","DOI":"10.1007\/978-3-030-32956-3"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"596","DOI":"10.1109\/ACCESS.2024.3522122","article-title":"Application of Deep Dictionary Learning and Predefined Filters for Classification of Retinal Optical Coherence Tomography Images","volume":"13","author":"Shaker","year":"2025","journal-title":"IEEE Access"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"3431","DOI":"10.1109\/JBHI.2020.2983730","article-title":"UD-MIL: Uncertainty-Driven Deep Multiple Instance Learning for OCT Image Classification","volume":"24","author":"Wang","year":"2020","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1117\/1.JBO.23.3.035005","article-title":"Automatic diagnosis of abnormal macula in retinal optical coherence tomography images using wavelet-based convolutional neural network features and random forests classifier","volume":"23","author":"Rasti","year":"2018","journal-title":"J. Biomed. Opt."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1959","DOI":"10.1109\/TMI.2019.2898414","article-title":"Attention to Lesion: Lesion-Aware Convolutional Neural Network for Retinal Optical Coherence Tomography Image Classification","volume":"38","author":"Fang","year":"2019","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1793","DOI":"10.1109\/LSP.2019.2949388","article-title":"Multi-Level Dual-Attention Based CNN for Macular Optical Coherence Tomography Classification","volume":"26","author":"Mishra","year":"2019","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"625","DOI":"10.1109\/TAI.2021.3135797","article-title":"Perturbed Composite Attention Model for Macular Optical Coherence Tomography Image Classification","volume":"3","author":"Mishra","year":"2022","journal-title":"IEEE Trans. Artif. Intell."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Liu, X., Bai, Y., Cao, J., Yao, J., Zhang, Y., and Wang, M. (2022). Joint disease classification and lesion segmentation via one-stage attention-based convolutional neural network in OCT images. Biomed. Signal Process. Control, 71 Part A.","DOI":"10.1016\/j.bspc.2021.103087"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Huang, X., Ai, Z., Wang, H., She, C., Feng, J., Wei, Q., Hao, B., Tao, Y., Lu, Y., and Zeng, F. (2023). GABNet: Global attention block for retinal OCT disease classification. Front. Neurosci., 17.","DOI":"10.3389\/fnins.2023.1143422"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1025","DOI":"10.1109\/LSP.2020.3000933","article-title":"B-Scan Attentive CNN for the Classification of Retinal Optical Coherence Tomography Volumes","volume":"27","author":"Das","year":"2020","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_38","first-page":"5830766","article-title":"Medical Image Classification Utilizing Ensemble Learning and Levy Flight-Based Honey Badger Algorithm on 6G-Enabled Internet of Things","volume":"2022","author":"Mabrouk","year":"2022","journal-title":"Comput. Intell. Neurosci."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Hassan, B., Hassan, T., Li, B., Ahmed, R., and Hassan, O. (2019). Deep Ensemble Learning Based Objective Grading of Macular Edema by Extracting Clinically Significant Findings from Fused Retinal Imaging Modalities. Sensors, 19.","DOI":"10.3390\/s19132970"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Das, V., Dandapat, S., and Bora, P.K. (2019). Multi-scale deep feature fusion for automated classification of macular pathologies from OCT images. Biomed. Signal Process. Control, 54.","DOI":"10.1016\/j.bspc.2019.101605"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Thomas, A., Harikrishnan, P., Ramachandran, R., Ramachandran, S., Manoj, R., Palanisamy, P., and Gopi, V.P. (2021). A novel multiscale and multipath convolutional neural network based age-related macular degeneration detection using OCT images. Comput. Methods Programs Biomed., 209.","DOI":"10.1016\/j.cmpb.2021.106294"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Thomas, A., Harikrishnan, P.M., Krishna, A.K., Palanisamy, P., and Gopi, V.P. (2021). A novel multiscale convolutional neural network based age-related macular degeneration detection using OCT images. Biomed. Signal Process. Control, 67.","DOI":"10.1016\/j.bspc.2021.102538"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Sotoudeh-Paima, S., Jodeiri, A., Hajizadeh, F., and Soltanian-Zadeh, H. (2022). Multi-scale convolutional neural network for automated AMD classification using retinal OCT images. Comput. Biol. Med., 144.","DOI":"10.1016\/j.compbiomed.2022.105368"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Akinniyi, O., Rahman, M.M., Sandhu, H.S., El-Baz, A., and Khalifa, F. (2023). Multi-Stage Classification of Retinal OCT Using Multi-Scale Ensemble Deep Architecture. Bioengineering, 10.","DOI":"10.3390\/bioengineering10070823"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1024","DOI":"10.1109\/TMI.2017.2780115","article-title":"Macular OCT Classification Using a Multi-Scale Convolutional Neural Network Ensemble","volume":"37","author":"Rasti","year":"2018","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"23256","DOI":"10.1109\/JSEN.2021.3108642","article-title":"Automated Classification of Retinal OCT Images Using a Deep Multi-Scale Fusion CNN","volume":"21","author":"Das","year":"2021","journal-title":"IEEE Sens. J."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"253","DOI":"10.1109\/JBHI.2018.2795545","article-title":"Surrogate-Assisted Retinal OCT Image Classification Based on Convolutional Neural Networks","volume":"23","author":"Rong","year":"2019","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"7000304","DOI":"10.1109\/LSENS.2019.2963712","article-title":"A Data-Efficient Approach for Automated Classification of OCT Images Using Generative Adversarial Network","volume":"4","author":"Das","year":"2020","journal-title":"IEEE Sens. Lett."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"8746","DOI":"10.1109\/JSEN.2020.2985131","article-title":"Unsupervised Super-Resolution of OCT Images Using Generative Adversarial Network for Improved Age-Related Macular Degeneration Diagnosis","volume":"20","author":"Das","year":"2020","journal-title":"IEEE Sens. J."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Ma, Z., Xie, Q., Gao, X., and Zhu, J. (2022). HCTNet: A Hybrid ConvNet-Transformer Network for Retinal Optical Coherence Tomography Image Classification. Biosensors, 12.","DOI":"10.3390\/bios12070542"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"He, J., Wang, J., Han, Z., Ma, J., Wang, C., and Qi, M. (2023). An interpretable transformer network for the retinal disease classification using optical coherence tomography. Sci. Rep., 13.","DOI":"10.1038\/s41598-023-30853-z"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"102608","DOI":"10.1016\/j.media.2022.102608","article-title":"Focused Attention in Transformers for interpretable classification of retinal images","volume":"82","author":"Playout","year":"2022","journal-title":"Med. Image Anal."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"e001423","DOI":"10.1136\/bmjophth-2023-001423","article-title":"Classification of diabetic maculopathy based on optical coherence tomography images using a Vision Transformer model","volume":"8","author":"Cai","year":"2023","journal-title":"BMJ Open Ophthalmol."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"105178","DOI":"10.1016\/j.ijmedinf.2023.105178","article-title":"MBT: Model-Based Transformer for retinal optical coherence tomography image and video multi-classification","volume":"178","author":"Hammou","year":"2023","journal-title":"Int. J. Med. Inform."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Shen, J., Hu, Y., Zhang, X., Gong, Y., Kawasaki, R., and Liu, J. (2023). Structure-Oriented Transformer for retinal diseases grading from OCT images. Comput. Biol. Med., 152.","DOI":"10.1016\/j.compbiomed.2022.106445"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"2532217","DOI":"10.1109\/TIM.2023.3329106","article-title":"OCTFormer: An Efficient Hierarchical Transformer Network Specialized for Retinal Optical Coherence Tomography Image Recognition","volume":"72","author":"Wang","year":"2023","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"9171","DOI":"10.1007\/s00521-024-09564-7","article-title":"Automated retinal disease classification using hybrid transformer model (SViT) using optical coherence tomography images","volume":"36","author":"Hemalakshmi","year":"2024","journal-title":"Neural. Comput. Applic."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Dutta, P., Sathi, K.A., Hossain, M.A., and Dewan, M.A.A. (2023). Conv-ViT: A Convolution and Vision Transformer-Based Hybrid Feature Extraction Method for Retinal Disease Detection. J. Imaging, 9.","DOI":"10.3390\/jimaging9070140"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"507","DOI":"10.1016\/j.bbe.2023.06.001","article-title":"Transformer-based cross-modal multi-contrast network for ophthalmic diseases diagnosis","volume":"43","author":"Yu","year":"2023","journal-title":"Biocybern. Biomed. Eng."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Li, Z., Han, Y., and Yang, X. (2023). Multi-Fundus Diseases Classification Using Retinal Optical Coherence Tomography Images with Swin Transformer V2. J. Imaging, 9.","DOI":"10.3390\/jimaging9100203"},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Wen, H., Zhao, J., Xiang, S., Lin, L., Liu, C., Wang, T., An, L., Liang, L., and Huang, B. (2022). Towards more efficient ophthalmic disease classification and lesion location via convolution transformer. Comput. Methods Programs Biomed., 220.","DOI":"10.1016\/j.cmpb.2022.106832"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Azizi, M.M., Abhari, S., and Sajedi, H. (2024). Stitched vision transformer for age-related macular degeneration detection using retinal optical coherence tomography images. PLoS ONE, 19.","DOI":"10.1371\/journal.pone.0304943"},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Ashtari-Majlan, M., Dehshibi, M.M., and Masip, D. (2024). Spatial-aware Transformer-GRU Framework for Enhanced Glaucoma Diagnosis from 3D OCT Imaging. arXiv.","DOI":"10.1109\/JBHI.2025.3550394"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"2250009","DOI":"10.1142\/S1793545822500092","article-title":"Computer-aided diagnosis of retinopathy based on vision transformer","volume":"15","author":"Jiang","year":"2022","journal-title":"J. Innov. Opt. Health Sci."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"100197","DOI":"10.1016\/j.xops.2022.100197","article-title":"Detection of Nonexudative Macular Neovascularization on Structural OCT Images Using Vision Transformers","volume":"2","author":"Kihara","year":"2022","journal-title":"Ophthalmol. Sci."},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Greenspan, H., Madabhushi, A., Mousavi, P., Salcudean, S., Duncan, J., Syeda-Mahmood, T., and Taylor, R. (2023). Transformer-Based End-to-End Classification of Variable-Length Volumetric Data. Medical Image Computing and Computer Assisted Intervention\u2013MICCAI 2023, Springer. Lecture Notes in Computer Science.","DOI":"10.1007\/978-3-031-43996-4"},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Zhou, Z., Niu, C., Yu, H., Zhao, J., Wang, Y., and Dai, C. (2023, January 28). Diagnosis of retinal diseases using the vision transformer model based on optical coherence tomography images. Proceedings of the SPIE 12601, SPIE-CLP Conference on Advanced Photonics 2022, San Diego, CA, USA.","DOI":"10.1117\/12.2665918"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"3568","DOI":"10.1364\/BOE.5.003568","article-title":"Fully automated detection of diabetic macular edema and dry age-related macular degeneration from optical coherence tomography images","volume":"5","author":"Srinivasan","year":"2014","journal-title":"Biomed. Opt. Express"},{"key":"ref_69","unstructured":"Sotoudeh-Paima, S. (2024, January 01). Labeled Retinal Optical Coherence Tomography Dataset for Classification of Normal, Drusen, and CNV Cases, Mendeley Data, V1. Available online: https:\/\/paperswithcode.com\/dataset\/labeled-retinal-optical-coherence-tomography."},{"key":"ref_70","unstructured":"Kermany, D., Zhang, K., and Goldbaum, M. (2024, January 01). Labeled Optical Coherence Tomography (OCT) and Chest X-Ray Images for Classification. Mendeley Data, V2. Available online: https:\/\/data.mendeley.com\/datasets\/rscbjbr9sj\/3."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"162","DOI":"10.1016\/j.ophtha.2013.07.013","article-title":"Quantitative Classification of Eyes with and without Intermediate Age-related Macular Degeneration Using Optical Coherence Tomography","volume":"121","author":"Farsiu","year":"2014","journal-title":"Ophthalmology"},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"748","DOI":"10.1016\/j.media.2011.06.005","article-title":"Automated macular pathology diagnosis in retinal OCT images using multi-scale spatial pyramid and local binary patterns in texture and shape encoding","volume":"15","author":"Liu","year":"2011","journal-title":"Med. Image Anal."},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Lema\u00eetre, G., Rastgoo, M., Massich, J., Sankar, S., M\u00e9riaudeau, F., and Sidib\u00e9, D. (2015, January 9). Classification of SD-OCT volumes with LBP: Application to DME detection. Proceedings of the Ophthalmic Medical Image Analysis Second International Workshop, OMIA 2015, Held in Conjunction with MICCAI2015, Munich, Germany.","DOI":"10.17077\/omia.1021"},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"106532","DOI":"10.1016\/j.compeleceng.2019.106532","article-title":"OCTID: Optical Coherence Tomography Image365 Database","volume":"81","author":"Gholami","year":"2020","journal-title":"Comput. Electr. Eng."},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Liew, A., Agaian, S., and Benbelkacem, S. (2023). Distinctions between Choroidal Neovascularization and Age Macular Degeneration in Ocular Disease Predictions via Multi-Size Kernels \u03becho-Weighted Median Patterns. Diagnostics, 13.","DOI":"10.3390\/diagnostics13040729"},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Liew, A., Ryan, L., and Agaian, S. (2022, January 27). Alpha mean trim texture descriptors for optical coherence tomography eye classification. Proceedings of the SPIE 12100, Multimodal Image Exploitation and Learning 2022, Orlando, FL, USA.","DOI":"10.1117\/12.2618059"},{"key":"ref_77","doi-asserted-by":"crossref","unstructured":"Xu, J., Yang, W., Wan, C., and Shen, J. (2020). Weakly supervised detection of central serous chorioretinopathy based on local binary patterns and discrete wavelet transform. Comput. Biol. Med., 127.","DOI":"10.1016\/j.compbiomed.2020.104056"},{"key":"ref_78","doi-asserted-by":"crossref","unstructured":"Yu, Y.-W., Lin, C.-H., Lu, C.-K., Wang, J.-K., and Huang, T.-L. (2022, January 7\u20139). Distinct Feature Labeling Methods for SVM-Based AMD Automated Detector on 3D OCT Volumes. Proceedings of the 2022 IEEE International Conference on Consumer Electronics (ICCE), Las Vegas, NV, USA.","DOI":"10.1109\/ICCE53296.2022.9730775"},{"key":"ref_79","doi-asserted-by":"crossref","unstructured":"Hussain, A., Bhuiyan, A., Luu, C.D., Smith, R.T., Guymer, R.H., Ishikawa, H., Schuman, J.S., Ramamohanarao, K., and Vavvas, D.G. (2018). Classification of healthy and diseased retina using SD-OCT imaging and Random Forest algorithm. PLoS ONE, 13.","DOI":"10.1371\/journal.pone.0198281"},{"key":"ref_80","doi-asserted-by":"crossref","unstructured":"Thomas, A., Sunija, A.P., Manoj, R., Ramachandran, R., Ramachandran, S., Varun, P.G., and Palanisamy, P. (2021). RPE layer detection and baseline estimation using statistical methods and randomization for classification of AMD from retinal OCT. Comput. Methods Programs Biomed., 200.","DOI":"10.1016\/j.cmpb.2020.105822"},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"1571","DOI":"10.1049\/iet-ipr.2018.6186","article-title":"Classification of dry age-related macular degeneration and diabetic macular oedema from optical coherence tomography images using dictionary","volume":"14","author":"Mousavi","year":"2020","journal-title":"IET Image Process."},{"key":"ref_82","doi-asserted-by":"crossref","unstructured":"Sun, Y., Li, S., and Sun, Z. (2017). Fully automated macular pathology detection in retina optical coherence tomography images using sparse coding and dictionary learning. J. Biomed. Opt., 22.","DOI":"10.1117\/1.JBO.22.1.016012"},{"key":"ref_83","doi-asserted-by":"crossref","unstructured":"Liew, A., Agaian, S., and Zhao, L. (2023, January 15). Mitigation of adversarial noise attacks on skin cancer detection via ordered statistics binary local features. Proceedings of the SPIE 12526, Multimodal Image Exploitation and Learning, Orlando, FL, USA.","DOI":"10.1117\/12.2664239"},{"key":"ref_84","doi-asserted-by":"crossref","unstructured":"Liew, A., Agaian, S.S., and Zhao, L. (2024, January 7). Enhancing the resilience of wireless capsule endoscopy imaging against adversarial contrast reduction using color quaternion modulus and phase patterns. Proceedings of the SPIE 13033, Multimodal Image Exploitation and Learning 2024, National Harbor, MD, USA.","DOI":"10.1117\/12.3013486"},{"key":"ref_85","unstructured":"Paschali, M., Conjeti, S., Navarro, F., and Navab, N. (2018). Generalizability vs. Robustness: Adversarial Examples for Medical Imaging. arXiv."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"33773","DOI":"10.1007\/s11042-023-14702-9","article-title":"Adversarial examples: Attacks and defences on medical deep learning systems","volume":"82","author":"Puttagunta","year":"2023","journal-title":"Multimed. Tools Appl."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"107332","DOI":"10.1016\/j.patcog.2020.107332","article-title":"Understanding adversarial attacks on deep learning based medical image analysis systems","volume":"110","author":"Ma","year":"2021","journal-title":"Pattern Recognit."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"1287","DOI":"10.1126\/science.aaw4399","article-title":"Adversarial attacks on medical machine learning emerging vulnerabilities demand new conversations","volume":"363","author":"Finlayson","year":"2019","journal-title":"Science"},{"key":"ref_89","doi-asserted-by":"crossref","unstructured":"Hirano, H., Minagi, A., and Takemoto, K. (2021). Universal adversarial attacks on deep neural networks for medical image classification. BMC Med. Imaging, 21.","DOI":"10.1186\/s12880-020-00530-y"},{"key":"ref_90","doi-asserted-by":"crossref","unstructured":"Chen, F., Wang, J., Liu, H., Kong, W., Zhao, Z., Ma, L., Liao, H., and Zhang, D. (2023). Frequency constraint-based adversarial attack on deep neural networks for medical image classification. Comput. Biol. Med., 164.","DOI":"10.1016\/j.compbiomed.2023.107248"},{"key":"ref_91","first-page":"200355","article-title":"MEFF\u2013A model ensemble feature fusion approach for tackling adversarial attacks in medical imaging","volume":"22","author":"Alzubaidi","year":"2024","journal-title":"Intell. Syst. Appl."},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"102605","DOI":"10.1016\/j.inffus.2024.102605","article-title":"Evidential dissonance measure in robust multi-view classification to resist adversarial attack","volume":"113","author":"Yue","year":"2025","journal-title":"Inf. Fusion"},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"120705","DOI":"10.1016\/j.ins.2024.120705","article-title":"A prior knowledge-guided distributionally robust optimization-based adversarial training strategy for medical image classification","volume":"673","author":"Jiang","year":"2024","journal-title":"Inf. Sci."},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"370","DOI":"10.1016\/j.neucom.2019.10.085","article-title":"Anatomical context protects deep learning from adversarial perturbations in medical imaging","volume":"379","author":"Li","year":"2020","journal-title":"Neurocomputing"},{"key":"ref_95","unstructured":"Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K. (2018). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. arXiv."},{"key":"ref_96","unstructured":"Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozi\u00e8re, B., Goyal, N., Hambro, E., and Azhar, F. (2023). LLaMA: Open and Efficient Foundation Language Models. arXiv."},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"2886","DOI":"10.1038\/s41591-024-03139-8","article-title":"Integrated image-based deep learning and language models for primary diabetes care","volume":"30","author":"Li","year":"2024","journal-title":"Nat. Med."},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"1384","DOI":"10.1136\/bjo-2023-325054","article-title":"Unveiling the Clinical Incapabilities: A Benchmarking Study of GPT-4V(ision) for Ophthalmic Multimodal Image Analysis","volume":"108","author":"Xu","year":"2024","journal-title":"Br. J. Ophthalmol."},{"key":"ref_99","doi-asserted-by":"crossref","first-page":"e51926","DOI":"10.2196\/51926","article-title":"Uncovering Language Disparity of ChatGPT on Retinal Vascular Disease Classification: Cross-Sectional Study","volume":"26","author":"Liu","year":"2024","journal-title":"J. Med. Internet Res."},{"key":"ref_100","first-page":"e40765","article-title":"Eyes on AI: ChatGPT\u2019s Transformative Potential Impact on Ophthalmology","volume":"15","author":"Dossantos","year":"2023","journal-title":"Cureus"}],"container-title":["Bioengineering"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2306-5354\/12\/9\/914\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T18:32:33Z","timestamp":1760034753000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2306-5354\/12\/9\/914"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,25]]},"references-count":100,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2025,9]]}},"alternative-id":["bioengineering12090914"],"URL":"https:\/\/doi.org\/10.3390\/bioengineering12090914","relation":{},"ISSN":["2306-5354"],"issn-type":[{"value":"2306-5354","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,8,25]]}}}