A Comprehensive Review: AI Techniques in Ophthalmic Imaging Analysis
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Abstract
In recent years, there has been significant progress in Artificial Intelligence (AI)-based technologies that can facilitate the accurate analysis of ophthalmic images. With the help of computer vision, the early diagnosis of eye diseases such as Diabetic Retinopathy (DR), cataracts, glaucoma, and Age-Related Macular Degeneration (AMD) has become possible. Nevertheless, overlapping symptoms and signs appear in different clinical cases, making it difficult for health care providers to make an accurate diagnosis. As a result, patients might fail to get early treatment, resulting in worse outcomes and the rise in the incidence of these diseases. Moreover, conventional methods are usually costly, l abor-intensive, and require the involvement of a professional ophthalmologist who has sufficient knowledge and skills to diagnose the condition. The objective of this paper is to conduct a literature review on the current status of AI technology-based approaches used to differentiate among the most prevalent types of ocular diseases by exploring relevant literature published between 2020 and 2025, analyzing the benefits and drawbacks of proposed methods and comparing them to other existing tools, and describing popular databases that researchers use to test the performance of their models. An important finding of this study is the ability of modern computer-aided diagnosis (CAD) systems to accurately identify vision-threatening diseases, which may outperform expert ophthalmologists. On the other hand, there are several challenges that need to be addressed in future studies, such as data imbalance, generalizability, validation, and expensive computation.
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[1] R. Alsohemi and S. Dardouri, “Fundus Image-Based Eye Disease Detection Using EfficientNetB3 Architec-ture,” J. Imaging, vol. 11, no. 8, p. 279, Aug. 2025, doi: 10.3390/jimaging11080279.
[2] P. Zang, T. T. Hormel, T. S. Hwang, S. T. Bailey, D. Huang, and Y. Jia, “Deep-Learning–Aided Diagnosis of Diabetic Retinopathy, Age-Related Macular Degeneration, and Glaucoma Based on Structural and Angio-graphic OCT,” Ophthalmol. Sci., vol. 3, no. 1, p. 100245, Mar. 2023, doi: 10.1016/j.xops.2022.100245.
[3] S. Srinivasan et al., “Inter-observer agreement in grading severity of diabetic retinopathy in wide-field fundus photographs,” Eye, vol. 37, no. 6, pp. 1231–1235, Apr. 2023, doi: 10.1038/s41433-022-02107-1.
[4] F. Rizvi, A. Rizvi, and A. V. Das, “Can artificial intelligence improve accessibility to ophthalmic image screening and diagnosis in low- and middle-income countries: a review,” Expert Rev. Ophthalmol., vol. 19, no. 3, pp. 159–161, May 2024, doi: 10.1080/17469899.2024.2321936.
[5] R. Mahum, S. U. Rehman, O. D. Okon, A. Alabrah, T. Meraj, and H. T. Rauf, “A Novel Hybrid Approach Based on Deep CNN to Detect Glaucoma Using Fundus Imaging,” Electronics, vol. 11, no. 1, p. 26, Dec. 2021, doi: 10.3390/electronics11010026.
[6] N. Kumar, M. Sharma, V. P. Singh, C. Madan, and S. Mehandia, “An empirical study of handcrafted and dense feature extraction techniques for lung and colon cancer classification from histopathological images,” Bio-med. Signal Process. Control, vol. 75, p. 103596, May 2022, doi: 10.1016/j.bspc.2022.103596.
[7] Puneet, R. Kumar, and M. Gupta, “Optical coherence tomography image based eye disease detection using deep convolutional neural network,” Health Inf. Sci. Syst., vol. 10, no. 1, p. 13, Jun. 2022, doi: 10.1007/s13755-022-00182-y.
[8] M. Alghamdi and M. Abdel-Mottaleb, “A Comparative Study of Deep Learning Models for Diagnosing Glau-coma From Fundus Images,” IEEE Access, vol. 9, pp. 23894–23906, 2021, doi: 10.1109/ACCESS.2021.3056641.
[9] M. K. Jabbar, J. Yan, H. Xu, Z. Ur Rehman, and A. Jabbar, “Transfer Learning-Based Model for Diabetic Reti-nopathy Diagnosis Using Retinal Images,” Brain Sci., vol. 12, no. 5, p. 535, Apr. 2022, doi: 10.3390/brainsci12050535.
[10] J.-H. Wu, N. D. Koseoglu, C. Jones, and T. Y. A. Liu, “Vision transformers: The next frontier for deep learn-ing-based ophthalmic image analysis,” Saudi J. Ophthalmol., vol. 37, no. 3, pp. 173–178, Jul. 2023, doi: 10.4103/sjopt.sjopt_91_23.
[11] R. Azad et al., “Advances in medical image analysis with vision Transformers: A comprehensive review,” Med. Image Anal., vol. 91, p. 103000, Jan. 2024, doi: 10.1016/j.media.2023.103000.
[12] S. C. Sonmez, M. Sevgi, F. Antaki, J. Huemer, and P. A. Keane, “Generative artificial intelligence in ophthal-mology: current innovations, future applications and challenges,” Br. J. Ophthalmol., vol. 108, no. 10, pp. 1335–1340, Oct. 2024, doi: 10.1136/bjo-2024-325458.
[13] K. Kazemzadeh, “Artificial intelligence in ophthalmology: opportunities, challenges, and ethical considera-tions,” Med. Hypothesis Discov. Innov. Ophthalmol., vol. 14, no. 1, pp. 255–272, May 2025, doi: 10.51329/mehdiophthal1517.
[14] D. Shu et al., “Role of Oxidative Stress in Ocular Diseases: A Balancing Act,” Metabolites, vol. 13, no. 2, p. 187, Jan. 2023, doi: 10.3390/metabo13020187.
[15] P. Kumar, R. Kumar, and M. Gupta, “Deep Learning Based Analysis of Ophthalmology: A Systematic Re-view,” EAI Endorsed Trans. Pervasive Health Technol., vol. 7, no. 29, p. e4, Sep. 2021, doi: 10.4108/eai.10-9-2021.170950.
[16] A. Das, R. Giri, G. Chourasia, and A. A. Bala, “Classification of Retinal Diseases Using Transfer Learning Ap-proach,” in 2019 International Conference on Communication and Electronics Systems (ICCES), Coimbatore, India: IEEE, Jul. 2019, pp. 2080–2084. doi: 10.1109/ICCES45898.2019.9002415.
[17] B. Hassan et al., “A comprehensive review of artificial intelligence models for screening major retinal diseas-es,” Artif. Intell. Rev., vol. 57, no. 5, p. 111, Apr. 2024, doi: 10.1007/s10462-024-10736-z.
[18] K. Delaunay et al., “Glial cells of the human fovea,” Mol. Vis., vol. 26, pp. 235–245, 2020.
[19] C. M. Travieso-Gonzalez, Applications of Pattern Recognition. IntechOpen, 2021. doi: 10.5772/intechopen.80151.
[20] L. Zhang, “Transfer learning approach for diabetic retinopathy detection using residual network,” Appl. Com-put. Eng., vol. 49, pp. 324–331, Mar. 2024, doi: 10.54254/2755-2721/49/20241698.
[21] K. Jain and M. Bharkatiya, “Nanoparticulate based ocular drug delivery technology: ‘Nanoemulsion a novel approach,’” Indian J. Pharm. Pharmacol., vol. 7, no. 3, pp. 168–176, Oct. 2020, doi: 10.18231/j.ijpp.2020.029.
[22] M. Zeppieri et al., “Optical Coherence Tomography (OCT): A Brief Look at the Uses and Technological Evo-lution of Ophthalmology,” Medicina (Mex.), vol. 59, no. 12, p. 2114, Dec. 2023, doi: 10.3390/medicina59122114.
[23] T. Yu et al., “A Systematic Review of Advances in AI-Assisted Analysis of Fundus Fluorescein Angiography (FFA) Images: From Detection to Report Generation,” Ophthalmol. Ther., vol. 14, no. 4, pp. 599–619, Apr. 2025, doi: 10.1007/s40123-025-01109-y.
[24] N. Unno et al., “Comparison of Multicolor Scanning Laser Imaging and Color Fundus Photography in Evaluat-ing Vessel Whitening in Branch Retinal Vein Occlusion,” Ophthalmic Res., vol. 66, no. 1, pp. 413–420, 2023, doi: 10.1159/000528251.
[25] J. He, C. Li, J. Ye, Y. Qiao, and L. Gu, “Multi-label ocular disease classification with a dense correlation deep neural network,” Biomed. Signal Process. Control, vol. 63, p. 102167, Jan. 2021, doi: 10.1016/j.bspc.2020.102167.
[26] H. Trinh et al., “Artificial intelligence techniques in inherited retinal diseases: a review,” Biomed. Phys. Eng. Express, vol. 11, no. 4, p. 042004, Jul. 2025, doi: 10.1088/2057-1976/ade9c7.
[27] M. M. Butt, D. N. F. A. Iskandar, S. E. Abdelhamid, G. Latif, and R. Alghazo, “Diabetic Retinopathy Detection from Fundus Images of the Eye Using Hybrid Deep Learning Features,” Diagnostics, vol. 12, no. 7, p. 1607, Jul. 2022, doi: 10.3390/diagnostics12071607.
[28] I. Qureshi, J. Ma, and Q. Abbas, “Diabetic retinopathy detection and stage classification in eye fundus images using active deep learning,” Multimed. Tools Appl., vol. 80, no. 8, pp. 11691–11721, Mar. 2021, doi: 10.1007/s11042-020-10238-4.
[29] “World report on vision.” Accessed: Sep. 17, 2025. [Online]. Available: https://www.who.int/publications/i/item/9789241516570
[30] M. Bangar and P. Chaudhary, “A novel approach for the classification of diabetic maculopathy using discrete wavelet transforms and a support vector machine,” AIMS Electron. Electr. Eng., vol. 7, no. 1, pp. 1–13, 2022, doi: 10.3934/electreng.2023001.
[31] R. Kashyap, R. Nair, S. M. P. Gangadharan, M. Botto-Tobar, S. Farooq, and A. Rizwan, “Glaucoma Detection and Classification Using Improved U-Net Deep Learning Model,” Healthcare, vol. 10, no. 12, p. 2497, Dec. 2022, doi: 10.3390/healthcare10122497.
[32] S.-H. Hung, W.-T. Yen, and D.-W. Lu, “Advances in Glaucoma Diagnosis and Treatment: Integrating Innova-tions for Enhanced Patient Outcomes,” Biomedicines, vol. 13, no. 4, p. 850, Apr. 2025, doi: 10.3390/biomedicines13040850.
[33] H. A. Albarqi, A. Garg, M. Z. Ahmad, A. A. Alqahtani, I. A. Walbi, and J. Ahmad, “Recent Progress in Chi-tosan-Based Nanomedicine for Its Ocular Application in Glaucoma,” Pharmaceutics, vol. 15, no. 2, p. 681, Feb. 2023, doi: 10.3390/pharmaceutics15020681.
[34] S. M. Saqib et al., “Cataract and glaucoma detection based on Transfer Learning using MobileNet,” Heliyon, vol. 10, no. 17, p. e36759, Sep. 2024, doi: 10.1016/j.heliyon.2024.e36759.
[35] H. Çeti̇Ner, “Cataract disease classification from fundus images with transfer learning based deep learning model on two ocular disease datasets,” Gümüşhane Üniversitesi Fen Bilim. Enstitüsü Derg., Jan. 2023, doi: 10.17714/gumusfenbil.1168842.
[36] K. Pesudovs et al., “Cataract-related blindness and vision impairment in 2020 and trends over time in relation to VISION 2020: the Right to Sight: an analysis for the Global Burden of Disease Study.,” Invest. Ophthalmol. Vis. Sci., vol. 62, no. 8, p. 3523, Jun. 2021.
[37] A. Sirajudeen, A. Balasubramaniam, and S. Karthikeyan, “Novel angular binary pattern (NABP) and kernel based convolutional neural networks classifier for cataract detection,” Multimed. Tools Appl., vol. 81, no. 27, pp. 38485–38512, Nov. 2022, doi: 10.1007/s11042-022-13092-8.
[38] Y.-M. Chen, W.-T. Huang, W.-H. Ho, and J.-T. Tsai, “Classification of age-related macular degeneration using convolutional-neural-network-based transfer learning,” BMC Bioinformatics, vol. 22, no. S5, p. 99, Nov. 2021, doi: 10.1186/s12859-021-04001-1.
[39] A. Arbab, A. Habibi, H. Rabbani, and M. Tajmirriahi, “From Image to Sequence: Exploring Vision Transform-ers for Optical Coherence Tomography Classification,” J. Med. Signals Sens., vol. 15, no. 6, Jun. 2025, doi: 10.4103/jmss.jmss_58_24.
[40] “What Is Macular Degeneration?,” Cleveland Clinic. Accessed: Mar. 17, 2026. [Online]. Available: https://my.clevelandclinic.org/health/diseases/15246-macular-degeneration
[41] Y. Tong, W. Lu, Y. Yu, and Y. Shen, “Application of machine learning in ophthalmic imaging modalities,” Eye Vis., vol. 7, no. 1, p. 22, Dec. 2020, doi: 10.1186/s40662-020-00183-6.
[42] P. K. Karn and W. H. Abdulla, “On Machine Learning in Clinical Interpretation of Retinal Diseases Using OCT Images,” Bioengineering, vol. 10, no. 4, p. 407, Mar. 2023, doi: 10.3390/bioengineering10040407.
[43] E. Tonti et al., “Artificial Intelligence and Advanced Technology in Glaucoma: A Review,” J. Pers. Med., vol. 14, no. 10, p. 1062, Oct. 2024, doi: 10.3390/jpm14101062.
[44] M. Ashtari-Majlan, M. M. Dehshibi, and D. Masip, “Deep Learning and Computer Vision for Glaucoma Detec-tion: A Review,” Jul. 31, 2023, arXiv: arXiv:2307.16528. doi: 10.48550/arXiv.2307.16528.
[45] C. Mohanty et al., “Using Deep Learning Architectures for Detection and Classification of Diabetic Retinopa-thy,” Sensors, vol. 23, no. 12, p. 5726, Jun. 2023, doi: 10.3390/s23125726.
[46] M. A. Al Jbaar and S. A. Dawwd, “DCNN-based embedded models for parallel diagnosis of ocular diseases,” East.-Eur. J. Enterp. Technol., vol. 4, no. 2 (124), pp. 53–69, Aug. 2023, doi: 10.15587/1729-4061.2023.281790.
[47] S. M. Khan et al., “A global review of publicly available datasets for ophthalmological imaging: barriers to access, usability, and generalisability,” Lancet Digit. Health, vol. 3, no. 1, pp. e51–e66, Jan. 2021, doi: 10.1016/S2589-7500(20)30240-5.
[48] “DRIVE - Grand Challenge,” grand-challenge.org. Accessed: Mar. 28, 2026. [Online]. Available: https://drive.grand-challenge.org/
[49] “The STARE Project.” Accessed: Mar. 28, 2026. [Online]. Available: https://cecas.clemson.edu/~ahoover/stare/
[50] “CHASE_DB1.” [Online]. Available: https://blogs.kingston.ac.uk/retinal/chasedb1/
[51] “CHASEDB1.” Accessed: Mar. 28, 2026. [Online]. Available: https://www.kaggle.com/datasets/khoongweihao/chasedb1
[52] “High-Resolution Fundus (HRF) Image Database.” [Online]. Available: https://www5.cs.fau.de/research/data/fundus-images/
[53] “Download IOSTAR Retinal Vessel Segmentation Dataset « RetinaCheck.” Accessed: Mar. 28, 2026. [Online]. Available: http://www.retinacheck.org/download-iostar-retinal-vessel-segmentation-dataset
[54] G. P. MAFFRE Gervais GAUTHIER, Bruno LAY, Julien ROGER, Damien ELIE, Mélanie FOLTETE, Arthur DONJON, Hugo, “Messidor,” ADCIS. Accessed: Mar. 28, 2026. [Online]. Available: https://www.adcis.net/en/third-party/messidor/
[55] G. P. MAFFRE Gervais GAUTHIER, Bruno LAY, Julien ROGER, Damien ELIE, Mélanie FOLTETE, Arthur DONJON, Hugo, “Messidor-2,” ADCIS. Accessed: Mar. 28, 2026. [Online]. Available: https://www.adcis.net/en/third-party/messidor2/
[56] “Diabetic Retinopathy Detection.” Accessed: Mar. 28, 2026. [Online]. Available: https://kaggle.com/diabetic-retinopathy-detection
[57] “DDR dataset.” Accessed: Mar. 28, 2026. [Online]. Available: https://www.kaggle.com/datasets/mariaherrerot/ddrdataset
[58] “IDRiD - Grand Challenge,” grand-challenge.org. Accessed: Mar. 28, 2026. [Online]. Available: https://idrid.grand-challenge.org/Rules/
[59] “APTOS 2019 Blindness Detection.” Accessed: Mar. 28, 2026. [Online]. Available: https://kaggle.com/aptos2019-blindness-detection
[60] “DiaRetDB1 V2.1.” Accessed: Mar. 28, 2026. [Online]. Available: https://www.kaggle.com/datasets/nguyenhung1903/diaretdb1-v21
[61] J. Ye, “GRAPE: A multi-modal glaucoma dataset of follow-up visual field and fundus images for glaucoma management,” Aug. 2023, doi: 10.6084/m9.figshare.c.6406319.v1.
[62] “Glaucoma Fundus Imaging Datasets.” Accessed: Mar. 29, 2026. [Online]. Available: https://www.kaggle.com/datasets/arnavjain1/glaucoma-datasets
[63] “REFUGE - Grand Challenge,” grand-challenge.org. Accessed: Mar. 29, 2026. [Online]. Available: https://refuge.grand-challenge.org/
[64] “CNNs for Automatic Glaucoma Assessment using Fundus Images: An Extensive Validation.” figshare. Ac-cessed: Mar. 29, 2026. [Online]. Available: https://figshare.com/s/c2d31f850af14c5b5232
[65] “Drishti-GS - RETINA DATASET FOR ONH SEGMENTATION.” Accessed: Mar. 29, 2026. [Online]. Availa-ble: https://www.kaggle.com/datasets/lokeshsaipureddi/drishtigs-retina-dataset-for-onh-segmentation
[66] “Drishti-GS Dataset Webpage.” Accessed: Mar. 29, 2026. [Online]. Available: https://cvit.iiit.ac.in/projects/mip/drishti-gs/mip-dataset2/Home.php
[67] “MIAG ULL.” Accessed: Mar. 29, 2026. [Online]. Available: https://medimrg.webs.ull.es/
[68] “ORIGA for OD and OC segmentation_数据集-飞桨AI Studio星河社区.” Accessed: Mar. 29, 2026. [Online]. Available: https://aistudio.baidu.com/datasetdetail/122940
[69] “Glaucoma Fundus Imaging Datasets.” Accessed: Mar. 29, 2026. [Online]. Available: https://www.kaggle.com/datasets/arnavjain1/glaucoma-datasets
[70] “PALM - Grand Challenge,” grand-challenge.org. Accessed: Mar. 29, 2026. [Online]. Available: https://palm.grand-challenge.org/
[71] “HPMI: A retinal fundus image dataset for identification of high and pathological myopia based on deep learning.” figshare, Dec. 13, 2023. doi: 10.6084/m9.figshare.24800232.v2.
[72] “RIADD (ISBI-2021) - Grand Challenge,” grand-challenge.org. Accessed: Mar. 29, 2026. [Online]. Available: https://riadd.grand-challenge.org/Home/
[73] “Retinal Disease Classification.” Accessed: Mar. 29, 2026. [Online]. Available: https://www.kaggle.com/datasets/andrewmvd/retinal-disease-classification
[74] “1000 Fundus images with 39 categories.” Accessed: Mar. 29, 2026. [Online]. Available: https://www.kaggle.com/datasets/linchundan/fundusimage1000
[75] “Ocular Disease Recognition.” Accessed: Mar. 29, 2026. [Online]. Available: https://www.kaggle.com/datasets/andrewmvd/ocular-disease-recognition-odir5k
[76] “ODIR-2019 - Grand Challenge,” grand-challenge.org. Accessed: Mar. 29, 2026. [Online]. Available: https://odir2019.grand-challenge.org/dataset/
[77] D. Kermany, K. Zhang, and M. Goldbaum, “Labeled Optical Coherence Tomography (OCT) and Chest X-Ray Images for Classification,” vol. 2, Jan. 2018, doi: 10.17632/rscbjbr9sj.2.
[78] “Farsiu Ophthalmology 2013.” Accessed: Mar. 29, 2026. [Online]. Available: https://people.duke.edu/~sf59/RPEDC_Ophth_2013_dataset.htm
[79] “octid-dataset.” Accessed: Mar. 29, 2026. [Online]. Available: https://www.kaggle.com/datasets/kawtarnaim/octid-dataset
[80] U. of W. Peyman Gholami and U. of W. Vasudevan Lakshminarayanan, “Optical Coherence Tomography Im-age Retinal Database.” Inter-university Consortium for Political and Social Research (ICPSR), Feb. 20, 2019. doi: 10.3886/E108503V1.
[81] “OCTDL: Retinal OCT Images dataset.” Accessed: Mar. 29, 2026. [Online]. Available: https://www.kaggle.com/datasets/shakilrana/octdl-retinal-oct-images-dataset
[82] “Chiu_BOE_2015.” Accessed: Mar. 29, 2026. [Online]. Available: https://people.duke.edu/~sf59/Chiu_BOE_2014_dataset.htm
[83] S. Sotoudeh-Paima, F. Hajizadeh, and H. Soltanian-Zadeh, “Labeled Retinal Optical Coherence Tomography Dataset for Classification of Normal, Drusen, and CNV Cases,” vol. 1, Oct. 2021, doi: 10.17632/8kt969dhx6.1.
[84] A. Montoro-Montarroso et al., “Fighting disinformation with artificial intelligence: fundamentals, advances and challenges,” El Prof. Inf., p. e320322, Jun. 2023, doi: 10.3145/epi.2023.may.22.
[85] A. F. A. H. Alnuaimi and T. H. K. Albaldawi, “An overview of machine learning classification techniques,” BIO Web Conf., vol. 97, p. 00133, 2024, doi: 10.1051/bioconf/20249700133.
[86] R. Seccia, S. Romano, M. Salvetti, A. Crisanti, L. Palagi, and F. Grassi, “Machine Learning Use for Prognostic Purposes in Multiple Sclerosis,” Life, vol. 11, no. 2, p. 122, Feb. 2021, doi: 10.3390/life11020122.
[87] P. Keramida, N. K. Syrigos, M. Kouvela, G. Poulakou, A. Charpidou, and O. Fiste, “AΙ-Driven Drug Repurpos-ing: Applications and Challenges,” Medicines, vol. 12, no. 4, p. 28, Nov. 2025, doi: 10.3390/medicines12040028.
[88] Y. El Ouahabi, M. H. E. Y. Alaoui, B. Nsiri, A. Soulaymani, A. Mokhtari, and B. Benaji, “Vigilance towards the use of artificial intelligence applications for breast cancer screening and early diagnosis,” E3S Web Conf., vol. 319, p. 01099, 2021, doi: 10.1051/e3sconf/202131901099.
[89] A. Inglis, A. C. Parnell, N. Subramani, and F. M. Doohan, “Machine Learning Applied to the Detection of My-cotoxin in Food: A Systematic Review,” Toxins, vol. 16, no. 6, p. 268, Jun. 2024, doi: 10.3390/toxins16060268.
[90] D. Valkenborg, A.-J. Rousseau, M. Geubbelmans, and T. Burzykowski, “Support vector machines,” Am. J. Orthod. Dentofacial Orthop., vol. 164, no. 5, pp. 754–757, Nov. 2023, doi: 10.1016/j.ajodo.2023.08.003.
[91] I. Karamanlis et al., “Deep Learning-Based Black Spot Identification on Greek Road Networks,” Data, vol. 8, no. 6, p. 110, Jun. 2023, doi: 10.3390/data8060110.
[92] A. B. Silva, E. D. S. Rocha, J. F. Lorenzato, and P. T. Endo, “Evaluating how different balancing data tech-niques impact on prediction of premature birth using machine learning models,” PLOS ONE, vol. 20, no. 3, p. e0316574, Apr. 2025, doi: 10.1371/journal.pone.0316574.
[93] W. Wongkaew, W. Muanyoksakul, C. Ngamkhanong, J. Sresakoolchai, and S. Kaewunruen, “Data driven ma-chine learning prognostics of buckling failure modes in ballasted railway track,” Discov. Appl. Sci., vol. 6, no. 4, p. 212, Apr. 2024, doi: 10.1007/s42452-024-05885-3.
[94] C. Singh, J. Morris, A. Rush, J. Gao, and Y. Deng, “Tree Prompting: Efficient Task Adaptation without Fine-Tuning,” in Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, Sin-gapore: Association for Computational Linguistics, 2023, pp. 6253–6267. doi: 10.18653/v1/2023.emnlp-main.384.
[95] M. A. Hafeez, M. Rashid, H. Tariq, Z. U. Abideen, S. S. Alotaibi, and M. H. Sinky, “Performance Improvement of Decision Tree: A Robust Classifier Using Tabu Search Algorithm,” Appl. Sci., vol. 11, no. 15, p. 6728, Jul. 2021, doi: 10.3390/app11156728.
[96] M. Georgioudakis and V. Plevris, “Response Spectrum Analysis of Multi-Story Shear Buildings Using Ma-chine Learning Techniques,” Computation, vol. 11, no. 7, p. 126, Jun. 2023, doi: 10.3390/computation11070126.
[97] M. N. U. Alam et al., “Machine learning in biological research: key algorithms, applications, and future direc-tions,” BMC Biol., vol. 23, no. 1, p. 324, Oct. 2025, doi: 10.1186/s12915-025-02424-3.
[98] F. Prezja et al., “Improving performance in colorectal cancer histology decomposition using deep and ensem-ble machine learning,” Heliyon, vol. 10, no. 18, p. e37561, Sep. 2024, doi: 10.1016/j.heliyon.2024.e37561.
[99] Y. Feng and J. Park, “Using machine learning-based binary classifiers for predicting organizational members’ user satisfaction with collaboration software,” PeerJ Comput. Sci., vol. 9, p. e1481, Jul. 2023, doi: 10.7717/peerj-cs.1481.
[100] F. D. E. Baran and M. Cetin, “AI-driven early diagnosis of specific mental disorders: a comprehensive study,” Cogn. Neurodyn., vol. 19, no. 1, p. 70, Dec. 2025, doi: 10.1007/s11571-025-10253-x.
[101] S. Nyawa, D. Tchuente, and S. Fosso-Wamba, “COVID-19 vaccine hesitancy: a social media analysis using deep learning,” Ann. Oper. Res., vol. 339, no. 1–2, pp. 477–515, Aug. 2024, doi: 10.1007/s10479-022-04792-3.
[102] M. Dirik, “Application of machine learning techniques for obesity prediction: a comparative study,” J. Com-plex. Health Sci., vol. 6, no. 2, pp. 16–34, Dec. 2023, doi: 10.21595/chs.2023.23193.
[103] M. Pal et al., “Early detection of human Mpox: A comparative study by using machine learning and deep learning models with ensemble approach,” Digit. Health, vol. 11, p. 20552076251344135, May 2025, doi: 10.1177/20552076251344135.
[104] N. A. Saran and F. Nar, “Fast binary logistic regression,” PeerJ Comput. Sci., vol. 11, p. e2579, Jan. 2025, doi: 10.7717/peerj-cs.2579.
[105] J. Musa and A. M. Abdulazeez, “A Review on Diabetes Classification Based on Machine Learning Algo-rithms,” Indones. J. Comput. Sci., vol. 13, no. 2, Apr. 2024, doi: 10.33022/ijcs.v13i2.3886.
[106] W. Zhou, “A Random Traffic Assignment Model for Networks Based on Discrete Dynamic Bayesian Algo-rithms,” Discrete Dyn. Nat. Soc., vol. 2022, no. 1, p. 8998352, Jan. 2022, doi: 10.1155/2022/8998352.
[107] Y. Wei and M. Shangguan, “A review of deep learning based intrusion detection systems,” vol. 56, 2023, doi: 10.54097/hset.v56i.10104.
[108] A. E. Ezugwu et al., “Classical Machine Learning: Seventy Years of Algorithmic Learning Evolution,” Data Intell., vol. 7, no. 4, pp. 947–996, Dec. 2025, doi: 10.3724/2096-7004.di.2024.0051.
[109] R. Priyadarshi, R. Ranjan, A. Kumar Vishwakarma, T. Yang, and R. Singh Rathore, “Exploring the Frontiers of Unsupervised Learning Techniques for Diagnosis of Cardiovascular Disorder: A Systematic Review,” IEEE Access, vol. 12, pp. 139253–139272, 2024, doi: 10.1109/ACCESS.2024.3468163.
[110] L. Manduchi, M. Vandenhirtz, A. Ryser, and J. Vogt, “Tree Variational Autoencoders,” Nov. 17, 2023, arXiv: arXiv:2306.08984. doi: 10.48550/arXiv.2306.08984.
[111] A. Yaqoob, R. Musheer Aziz, and N. K. Verma, “Applications and Techniques of Machine Learning in Cancer Classification: A Systematic Review,” Hum.-Centric Intell. Syst., vol. 3, no. 4, pp. 588–615, Sep. 2023, doi: 10.1007/s44230-023-00041-3.
[112] Z. Lu, “Clustering Longitudinal Data: A Review of Methods and Software Packages,” Int. Stat. Rev., vol. 93, no. 3, pp. 425–458, Dec. 2025, doi: 10.1111/insr.12588.
[113] K. Kim, J. Kim, L. A. Wasserman, and E. H. Kennedy, “Hierarchical and Density-based Causal Clustering,” Nov. 02, 2024, arXiv: arXiv:2411.01250. doi: 10.48550/arXiv.2411.01250.
[114] H. Mittal, A. C. Pandey, M. Saraswat, S. Kumar, R. Pal, and G. Modwel, “A comprehensive survey of image segmentation: clustering methods, performance parameters, and benchmark datasets,” Multimed. Tools Appl., vol. 81, no. 24, pp. 35001–35026, Oct. 2022, doi: 10.1007/s11042-021-10594-9.
[115] B. Sasmal and K. G. Dhal, “A survey on the utilization of Superpixel image for clustering based image seg-mentation,” Multimed. Tools Appl., vol. 82, no. 23, pp. 35493–35555, Sep. 2023, doi: 10.1007/s11042-023-14861-9.
[116] A. Pegado-Bardayo, A. Lorenzo-Espejo, J. Muñuzuri, and A. Escudero-Santana, “A review of unsupervised k-value selection techniques in clustering algorithms,” J. Ind. Eng. Manag., vol. 17, no. 3, p. 641, Aug. 2024, doi: 10.3926/jiem.6791.
[117] N. Pillai, A. R. Das, M. Ayoola, G. Gireesan, B. Nanduri, and M. Ramkumar, “EndToEndML: An Open-Source End-to-End Pipeline for Machine Learning Applications,” Mar. 27, 2024, arXiv: arXiv:2403.18203. doi: 10.48550/arXiv.2403.18203.
[118] A. T. Abdel-Wahed, Z. Ullah, A. S. Abdel-Khalik, M. S. Hamad, S. Ahmed, and N. Elmalhy, “Drive Cycle-Based Design With the Aid of Data Mining Methods: A Review on Clustering Techniques of Electric Vehicle Motor Design With a Case Study,” IEEE Access, vol. 11, pp. 115775–115797, 2023, doi: 10.1109/ACCESS.2023.3325400.
[119] M. Drogkoula, K. Kokkinos, and N. Samaras, “A Comprehensive Survey of Machine Learning Methodologies with Emphasis in Water Resources Management,” Appl. Sci., vol. 13, no. 22, p. 12147, Nov. 2023, doi: 10.3390/app132212147.
[120] M. H. A. Khan, “STICA: Spanning-Tree-Based Identification of Clusters With Agglomeration for Numerical Data,” IEEE Access, vol. 12, pp. 150430–150450, 2024, doi: 10.1109/ACCESS.2024.3480460.
[121] D. Papakyriakou and I. S. Barbounakis, “Data Mining Methods: A Review,” Int. J. Comput. Appl., vol. 183, no. 48, pp. 5–19, Jan. 2022, doi: 10.5120/ijca2022921884.
[122] Q. Zheng, K. Zhang, Q. Chen, J. Wu, and J. Lin, “DKCDC: A clustering algorithm focusing on genuine bounda-ry search for regional division,” PLOS One, vol. 20, no. 9, p. e0331555, Sep. 2025, doi: 10.1371/journal.pone.0331555.
[123] K. Aggarwal et al., “Robust Assessment of Clustering Methods for Fast Radio Transient Candidates,” Astro-phys. J., vol. 914, no. 1, p. 53, Jun. 2021, doi: 10.3847/1538-4357/abf92b.
[124] T. Dinh et al., “Data clustering: a fundamental method in data science and management,” Data Sci. Manag., p. S2666764925000360, Aug. 2025, doi: 10.1016/j.dsm.2025.08.001.
[125] R. Patil and S. Sharma, “Automatic glaucoma detection from fundus images using transfer learning,” Mul-timed. Tools Appl., vol. 83, no. 32, pp. 78207–78226, Feb. 2024, doi: 10.1007/s11042-024-18242-8.
[126] A. García-Floriano and E. Ventura-Molina, “Age-Related Macular Degeneration Detection in Retinal Fundus Images by a Deep Convolutional Neural Network,” Mathematics, vol. 12, no. 10, p. 1445, May 2024, doi: 10.3390/math12101445.
[127] L. Cheng, “The Practice and Application of Machine Learning in Data Analysis,” J. Artif. Intell. Pract., vol. 8, no. 1, pp. 79–85, 2025, doi: 10.23977/jaip.2025.080111.
[128] I. H. Sarker, “Deep Learning: A Comprehensive Overview on Techniques, Taxonomy, Applications and Re-search Directions,” SN Comput. Sci., vol. 2, no. 6, p. 420, Nov. 2021, doi: 10.1007/s42979-021-00815-1.
[129] T. Perumal, N. Mustapha, R. Mohamed, and F. M. Shiri, “A Comprehensive Overview and Comparative Analy-sis on Deep Learning Models,” J. Artif. Intell., vol. 6, no. 1, pp. 301–360, 2024, doi: 10.32604/jai.2024.054314.
[130] E. R. AlBasiouny, A.-F. A. Heliel, H. E. Abdelmunim, and H. M. Abbas, “Multilayer Perceptron Generative Model via Adversarial Learning for Robust Visual Tracking,” IEEE Access, vol. 10, pp. 121230–121248, 2022, doi: 10.1109/ACCESS.2022.3222867.
[131] A. Salhi, R. Alshamrani, A. Althbiti, A. Ismail, M. Abd-ElRahman, and B. M. Hassan, “Optimizing high dimen-sional data classification with a hybrid AI driven feature selection framework and machine learning schema,” Sci. Rep., vol. 15, no. 1, p. 35038, Oct. 2025, doi: 10.1038/s41598-025-08699-4.
[132] D. Bhatt et al., “CNN Variants for Computer Vision: History, Architecture, Application, Challenges and Future Scope,” Electronics, vol. 10, no. 20, p. 2470, Oct. 2021, doi: 10.3390/electronics10202470.
[133] L. Alzubaidi et al., “Review of deep learning: concepts, CNN architectures, challenges, applications, future directions,” J. Big Data, vol. 8, no. 1, p. 53, Mar. 2021, doi: 10.1186/s40537-021-00444-8.
[134] A. W. Salehi et al., “A Study of CNN and Transfer Learning in Medical Imaging: Advantages, Challenges, Fu-ture Scope,” Sustainability, vol. 15, no. 7, p. 5930, Mar. 2023, doi: 10.3390/su15075930.
[135] M.E student from Madhav Institute of Technology and Science, Gwalior, Madhya Pradesh, India., A. Gupta, Dr. R. P. Narwaria, and Assistant Professor in MITS, Gwalior, Madhya Pradesh, India., “Review on Deep Learning Handwritten Digit Recognition using Convolutional Neural Network,” Int. J. Recent Technol. Eng. IJRTE, vol. 9, no. 5, pp. 245–247, Jan. 2021, doi: 10.35940/ijrte.E5287.019521.
[136] M. Z. Khan, M. K. Gajendran, Y. Lee, and M. A. Khan, “Deep Neural Architectures for Medical Image Seman-tic Segmentation: Review,” IEEE Access, vol. 9, pp. 83002–83024, 2021, doi: 10.1109/ACCESS.2021.3086530.
[137] M. B. Shaikh and D. Chai, “RGB-D Data-Based Action Recognition: A Review,” Sensors, vol. 21, no. 12, p. 4246, Jun. 2021, doi: 10.3390/s21124246.
[138] Y. Liu et al., “Brain model state space reconstruction using an LSTM neural network,” J. Neural Eng., vol. 20, no. 3, p. 036024, Jun. 2023, doi: 10.1088/1741-2552/acd871.
[139] A. Gutiérrez, J. Bobadilla, and S. Alons, “Comparison of Models for Predicting the Number of Calls Received in a Call Center through Time Series Analysis,” J. Adv. Inf. Technol., vol. 15, no. 11, pp. 1273–1282, 2024, doi: 10.12720/jait.15.11.1273-1282.
[140] W. Fang, Y. Chen, and Q. Xue, “Survey on Research of RNN-Based Spatio-Temporal Sequence Prediction Algorithms,” J. Big Data, vol. 3, no. 3, pp. 97–110, 2021, doi: 10.32604/jbd.2021.016993.
[141] S.-W. Park, J.-S. Ko, J.-H. Huh, and J.-C. Kim, “Review on Generative Adversarial Networks: Focusing on Computer Vision and Its Applications,” Electronics, vol. 10, no. 10, p. 1216, May 2021, doi: 10.3390/electronics10101216.
[142] Y. Zhao, “Application of deep learning for prediction of alzheimer’s disease in PET/MR imaging,” Bioengi-neering, vol. 10, no. 10, p. 1120, Sep. 2023, doi: 10.3390/bioengineering10101120.
[143] Y. Zhao et al., “Application of Deep Learning for Prediction of Alzheimer’s Disease in PET/MR Imaging,” Bioengineering, vol. 10, no. 10, p. 1120, Sep. 2023, doi: 10.3390/bioengineering10101120.
[144] V. Machaca et al., “Transformers meets neoantigen detection: a systematic literature review,” J. Integr. Bioin-forma., vol. 21, no. 2, p. 20230043, Aug. 2024, doi: 10.1515/jib-2023-0043.
[145] A. Rahali and M. A. Akhloufi, “End-to-End Transformer-Based Models in Textual-Based NLP,” AI, vol. 4, no. 1, pp. 54–110, Jan. 2023, doi: 10.3390/ai4010004.
[146] Z. Liu et al., “Swin Transformer: Hierarchical Vision Transformer using Shifted Windows,” in 2021 IEEE/CVF International Conference on Computer Vision (ICCV), Montreal, QC, Canada: IEEE, Oct. 2021, pp. 9992–10002. doi: 10.1109/ICCV48922.2021.00986.
[147] A. Bennetot et al., “A Practical Tutorial on Explainable AI Techniques,” ACM Comput. Surv., vol. 57, no. 2, pp. 1–44, Feb. 2025, doi: 10.1145/3670685.
[148] A. Vaswani et al., “Attention is All you Need,” NeurIPS, 2017.
[149] G. Papanastasiou, N. Dikaios, J. Huang, C. Wang, and G. Yang, “Is Attention all You Need in Medical Image Analysis? A Review,” IEEE J. Biomed. Health Inform., vol. 28, no. 3, pp. 1398–1411, Mar. 2024, doi: 10.1109/JBHI.2023.3348436.
[150] A. Dosovitskiy et al., “An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale,” Jun. 03, 2021, arXiv: arXiv:2010.11929. doi: 10.48550/arXiv.2010.11929.
[151] X. Yang, “An Overview of the Attention Mechanisms in Computer Vision,” J. Phys. Conf. Ser., vol. 1693, no. 1, p. 012173, Dec. 2020, doi: 10.1088/1742-6596/1693/1/012173.
[152] P. Jiang, X. Hou, and Y. Zhou, “Device Tuning for Multi-Task Large Model,” Feb. 21, 2023, arXiv: arXiv:2302.10820. doi: 10.48550/arXiv.2302.10820.
[153] K. Lu et al., “DataSculpt: Crafting Data Landscapes for Long-Context LLMs through Multi-Objective Parti-tioning,” Oct. 02, 2024, arXiv: arXiv:2409.00997. doi: 10.48550/arXiv.2409.00997.
[154] P. Xu, X. Zhu, and D. A. Clifton, “Multimodal Learning with Transformers: A Survey,” May 10, 2023, arXiv: arXiv:2206.06488. doi: 10.48550/arXiv.2206.06488.
[155] W. L. Alyoubi, M. F. Abulkhair, and W. M. Shalash, “Diabetic Retinopathy Fundus Image Classification and Lesions Localization System Using Deep Learning,” Sensors, vol. 21, no. 11, p. 3704, May 2021, doi: 10.3390/s21113704.
[156] J. Li et al., “An Interpretable High-Accuracy Method for Rice Disease Detection Based on Multisource Data and Transfer Learning,” Plants, vol. 12, no. 18, p. 3273, Sep. 2023, doi: 10.3390/plants12183273.
[157] Y. Hagiwara, K. Taguchi, S. Ishibushi, A. Taniguchi, and T. Taniguchi, “Hierarchical Bayesian model for the transfer of knowledge on spatial concepts based on multimodal information,” Adv. Robot., vol. 36, no. 1–2, pp. 33–53, Jan. 2022, doi: 10.1080/01691864.2021.2004224.
[158] Q. Chen, T. Leng, S. Niu, and E. Trucco, “Editorial: Generalizable and explainable artificial intelligence meth-ods for retinal disease analysis: challenges and future trends,” Front. Med., vol. 11, p. 1465369, Jul. 2024, doi: 10.3389/fmed.2024.1465369.
[159] S. Nazir, D. M. Dickson, and M. U. Akram, “Survey of explainable artificial intelligence techniques for bio-medical imaging with deep neural networks,” Comput. Biol. Med., vol. 156, p. 106668, Apr. 2023, doi: 10.1016/j.compbiomed.2023.106668.
[160] Y. Sabiri, W. Houmaidi, and A. Abouaomar, “EYE-DEX: Eye Disease Detection and EXplanation System,” Sep. 29, 2025, arXiv: arXiv:2509.24136. doi: 10.48550/arXiv.2509.24136.
[161] V. Sarao, D. Veritti, A. De Nardin, M. Misciagna, G. Foresti, and P. Lanzetta, “Explainable artificial intelli-gence model for the detection of geographic atrophy using colour retinal photographs,” BMJ Open Ophthal-mol., vol. 8, no. 1, p. e001411, Dec. 2023, doi: 10.1136/bmjophth-2023-001411.
[162] Y. A. Fahim, I. W. Hasani, S. Kabba, and W. M. Ragab, “Artificial intelligence in healthcare and medicine: clinical applications, therapeutic advances, and future perspectives,” Eur. J. Med. Res., vol. 30, no. 1, p. 848, Sep. 2025, doi: 10.1186/s40001-025-03196-w.
[163] S. Acosta-Jiménez et al., “Assessing ResNeXt and RegNet Models for Diabetic Retinopathy Classification: A Comprehensive Comparative Study,” Diagnostics, vol. 15, no. 15, p. 1966, Aug. 2025, doi: 10.3390/diagnostics15151966.
[164] M. I. Nazir, A. Akter, M. A. Hussen Wadud, and M. A. Uddin, “Utilizing customized CNN for brain tumor pre-diction with explainable AI,” Heliyon, vol. 10, no. 20, p. e38997, Oct. 2024, doi: 10.1016/j.heliyon.2024.e38997.
[165] Y. Li, X. Yi, J. Fu, Y. Yang, C. Duan, and J. Wang, “Reducing misdiagnosis in AI-driven medical diagnostics: a multidimensional framework for technical, ethical, and policy solutions,” Front. Med., vol. 12, p. 1594450, Oct. 2025, doi: 10.3389/fmed.2025.1594450.
[166] M. H. Udin et al., “Investigating methods to enhance interpretability and performance in cardiac MRI for my-ocardial scarring diagnosis using convolutional neural network classification and One Match,” PLOS One, vol. 20, no. 6, p. e0313971, Jun. 2025, doi: 10.1371/journal.pone.0313971.
[167] S. Iftikhar, N. Anjum, A. B. Siddiqui, M. Ur Rehman, and N. Ramzan, “Explainable CNN for brain tumor detec-tion and classification through XAI based key features identification,” Brain Inform., vol. 12, no. 1, p. 10, Dec. 2025, doi: 10.1186/s40708-025-00257-y.
[168] A. M. Mutawa, S. Alnajdi, and S. Sruthi, “Transfer Learning for Diabetic Retinopathy Detection: A Study of Dataset Combination and Model Performance,” Appl. Sci., vol. 13, no. 9, p. 5685, May 2023, doi: 10.3390/app13095685.
[169] “Detection and Diagnosis of Diabetic Retinopathy Using Transfer Learning Approach,” Int. J. Intell. Eng. Syst., vol. 16, no. 3, pp. 62–74, Jun. 2023, doi: 10.22266/ijies2023.0630.05.
[170] A. Jabbar et al., “Deep Transfer Learning-Based Automated Diabetic Retinopathy Detection Using Retinal Fundus Images in Remote Areas,” Int. J. Comput. Intell. Syst., vol. 17, no. 1, p. 135, May 2024, doi: 10.1007/s44196-024-00520-w.
[171] R. Bhoopalan, P. Sekar, N. Nagaprasad, T. R. Mamo, and R. Krishnaraj, “Task optimized vision transformer for diabetic retinopathy detection and classification in resource constrained early diagnosis settings,” Sci. Rep., vol. 15, no. 1, p. 39047, Nov. 2025, doi: 10.1038/s41598-025-25399-1.
[172] N. Varma, S. Yadav, and J. K. P. S. Yadav, “A reliable automatic cataract detection using deep learning,” Int. J. Syst. Assur. Eng. Manag., vol. 14, no. 3, pp. 1089–1102, Jun. 2023, doi: 10.1007/s13198-023-01923-2.
[173] S. M. Saqib et al., “Cataract and glaucoma detection based on Transfer Learning using MobileNet,” Heliyon, vol. 10, no. 17, p. e36759, Sep. 2024, doi: 10.1016/j.heliyon.2024.e36759.
[174] S. S. Mahmood, S. Chaabouni, and A. Fakhfakh, “Improving Automated Detection of Cataract Disease through Transfer Learning using ResNet50,” Eng. Technol. Appl. Sci. Res., vol. 14, no. 5, pp. 17541–17547, Oct. 2024, doi: 10.48084/etasr.8530.
[175] Z. Feng, K. Xu, L. Li, and Y. Wang, “Hybrid Deep Learning Model for Cataract Diagnosis Assistance,” Appl. Sci., vol. 14, no. 23, p. 11314, Dec. 2024, doi: 10.3390/app142311314.
[176] A. A. Jabber, A. Hadi, S. Wadi, and G. Ali, “Cataract Detection and Classification Using Deep Learning Tech-niques,” Int. J. Comput. Digit. Syst., vol. 17, no. 1, pp. 1–10, Jan. 2025, doi: 10.12785/ijcds/1571037888.
[177] L. K. Singh, Pooja, H. Garg, and M. Khanna, “Histogram of Oriented Gradients (HOG)-Based Artificial Neural Network (ANN) Classifier for Glaucoma Detection:,” Int. J. Swarm Intell. Res., vol. 13, no. 1, pp. 1–32, Oct. 2022, doi: 10.4018/IJSIR.309940.
[178] V. K. Velpula and L. D. Sharma, “Multi-stage glaucoma classification using pre-trained convolutional neural networks and voting-based classifier fusion,” Front. Physiol., vol. 14, p. 1175881, Jun. 2023, doi: 10.3389/fphys.2023.1175881.
[179] F. Chincholi and H. Koestler, “Transforming glaucoma diagnosis: transformers at the forefront,” Front. Artif. Intell., vol. 7, p. 1324109, Jan. 2024, doi: 10.3389/frai.2024.1324109.
[180] S. J. Edan and M.-R. Feizi-Derakhshi, “An Efficient Glaucoma Classification for Fundus Images by Pre-trained Deep CNNs and Accuracy-Weighted Voting Classifier Fusion,” Baghdad Sci. J., vol. 22, no. 8, pp. 2813–2823, Aug. 2025, doi: 10.21123/2411-7986.5040.
[181] Z. Wang, S. R. Sadda, A. Lee, and Z. J. Hu, “Automated segmentation and feature discovery of age-related macular degeneration and Stargardt disease via self-attended neural networks,” Sci. Rep., vol. 12, no. 1, p. 14565, Aug. 2022, doi: 10.1038/s41598-022-18785-6.
[182] S. S. Mishra, B. Mandal, and N. B. Puhan, “MacularNet: Towards Fully Automated Attention-Based Deep CNN for Macular Disease Classification,” SN Comput. Sci., vol. 3, no. 2, p. 142, Mar. 2022, doi: 10.1007/s42979-022-01024-0.
[183] J. Han et al., “Detecting Macular Disease Based on Optical Coherence Tomography Using a Deep Convolu-tional Network,” J. Clin. Med., vol. 12, no. 3, p. 1005, Jan. 2023, doi: 10.3390/jcm12031005.
[184] Md. A. Ali, Md. S. Hossain, Md. K. Hossain, S. S. Sikder, S. A. Khushbu, and M. Islam, “AMDNet23: Hybrid CNN-LSTM deep learning approach with enhanced preprocessing for age-related macular degeneration (AMD) detection,” Intell. Syst. Appl., vol. 21, p. 200334, Mar. 2024, doi: 10.1016/j.iswa.2024.200334.
[185] M. M. Azizi, S. Abhari, and H. Sajedi, “Stitched vision transformer for age-related macular degeneration de-tection using retinal optical coherence tomography images,” PLOS ONE, vol. 19, no. 6, p. e0304943, Jun. 2024, doi: 10.1371/journal.pone.0304943.
[186] E. Yusufoğlu et al., “A Comprehensive CNN Model for Age-Related Macular Degeneration Classification Us-ing OCT: Integrating Inception Modules, SE Blocks, and ConvMixer,” Diagnostics, vol. 14, no. 24, p. 2836, Dec. 2024, doi: 10.3390/diagnostics14242836.
[187] I. D. Mienye, T. G. Swart, G. Obaido, M. Jordan, and P. Ilono, “Deep Convolutional Neural Networks in Medi-cal Image Analysis: A Review,” Information, vol. 16, no. 3, p. 195, Mar. 2025, doi: 10.3390/info16030195.
[188] L. Alsadoun et al., “Artificial Intelligence (AI)-Enhanced Detection of Diabetic Retinopathy From Fundus Im-ages: The Current Landscape and Future Directions,” Cureus, Aug. 2024, doi: 10.7759/cureus.67844.
[189] J. Lo et al., “Federated Learning for Microvasculature Segmentation and Diabetic Retinopathy Classification of OCT Data,” Ophthalmol. Sci., vol. 1, no. 4, p. 100069, Dec. 2021, doi: 10.1016/j.xops.2021.100069.
[190] T. D. Nguyen, D.-T. Le, J. Bum, S. Kim, S. J. Song, and H. Choo, “Retinal Disease Diagnosis Using Deep Learn-ing on Ultra-Wide-Field Fundus Images,” Diagnostics, vol. 14, no. 1, p. 105, Jan. 2024, doi: 10.3390/diagnostics14010105.
[191] S. Paul and P.-Y. Chen, “Vision Transformers Are Robust Learners,” Proc. AAAI Conf. Artif. Intell., vol. 36, no. 2, pp. 2071–2081, Jun. 2022, doi: 10.1609/aaai.v36i2.20103.
[192] A. Kirillov et al., “Segment Anything,” in 2023 IEEE/CVF International Conference on Computer Vision (ICCV), Paris, France: IEEE, Oct. 2023, pp. 3992–4003. doi: 10.1109/ICCV51070.2023.00371.
[193] M. Kulyabin et al., “Segment Anything in Optical Coherence Tomography: SAM 2 for Volumetric Segmenta-tion of Retinal Biomarkers,” Bioengineering, vol. 11, no. 9, p. 940, Sep. 2024, doi: 10.3390/bioengineering11090940.
[194] P. Li, J. Tao, Q. Yuan, R. Zhang, and P. Gao, “Research progress in deep learning-based fundus image analysis for the diagnosis and prediction of hypertension-related diseases,” Front. Cell Dev. Biol., vol. 13, p. 1608994, Aug. 2025, doi: 10.3389/fcell.2025.1608994.
[195] B. Guo et al., “WaveAttention-ResNet: a deep learning-based intelligent diagnostic model for the auxiliary diagnosis of multiple retinal diseases,” Front. Radiol., vol. 5, p. 1608052, Jul. 2025, doi: 10.3389/fradi.2025.1608052.
[196] Z. Li, S. Yin, S. Wang, Y. Wang, W. Qiang, and J. Jiang, “Transformative applications of oculomics-based AI approaches in the management of systemic diseases: A systematic review,” J. Adv. Res., vol. 75, pp. 679–696, Sep. 2025, doi: 10.1016/j.jare.2024.11.018.