{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T15:23:42Z","timestamp":1774365822656,"version":"3.50.1"},"reference-count":97,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2025,8,18]],"date-time":"2025-08-18T00:00:00Z","timestamp":1755475200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Research Foundation (NRF)","award":["NRF [2022-R1-G1A1(010226)]"],"award-info":[{"award-number":["NRF [2022-R1-G1A1(010226)]"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>Ocular disease (OD) represents a complex medical condition affecting humans. OD diagnosis is a challenging process in the current medical system, and blindness may occur if the disease is not detected at its initial phase. Recent studies showed significant outcomes in the identification of OD using deep learning (DL) models. Thus, this work aims to develop a multi-classification DL-based model for the classification of seven ODs, including normal (NOR), age-related macular degeneration (AMD), diabetic retinopathy (DR), glaucoma (GLU), maculopathy (MAC), non-proliferative diabetic retinopathy (NPDR), and proliferative diabetic retinopathy (PDR), using color fundus images (CFIs). This work proposes a custom model named the ocular disease detection model (ODDM) based on a CNN. The proposed ODDM is trained and tested on a publicly available ocular disease dataset (ODD). Additionally, the SMOTE Tomek (SM-TOM) approach is also used to handle the imbalanced distribution of the OD images in the ODD. The performance of the ODDM is compared with seven baseline models, including DenseNet-201 (R1), EfficientNet-B0 (R2), Inception-V3 (R3), MobileNet (R4), Vgg-16 (R5), Vgg-19 (R6), and ResNet-50 (R7). The proposed ODDM obtained a 98.94% AUC, along with 97.19% accuracy, a recall of 88.74%, a precision of 95.23%, and an F1-score of 88.31% in classifying the seven different types of OD. Furthermore, ANOVA and Tukey HSD (Honestly Significant Difference) post hoc tests are also applied to represent the statistical significance of the proposed ODDM. Thus, this study concludes that the results of the proposed ODDM are superior to those of baseline models and state-of-the-art models.<\/jats:p>","DOI":"10.3390\/jimaging11080278","type":"journal-article","created":{"date-parts":[[2025,8,18]],"date-time":"2025-08-18T13:28:22Z","timestamp":1755523702000},"page":"278","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["ODDM: Integration of SMOTE Tomek with Deep Learning on Imbalanced Color Fundus Images for Classification of Several Ocular Diseases"],"prefix":"10.3390","volume":"11","author":[{"given":"Afraz Danish Ali","family":"Qureshi","sequence":"first","affiliation":[{"name":"Department of Computer Science, National College of Business Administration & Economics Lahore, Multan Sub Campus, Multan 60000, Pakistan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4402-5088","authenticated-orcid":false,"given":"Hassaan","family":"Malik","sequence":"additional","affiliation":[{"name":"Department of Computer Science, National College of Business Administration & Economics Lahore, Multan Sub Campus, Multan 60000, Pakistan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9705-386X","authenticated-orcid":false,"given":"Ahmad","family":"Naeem","sequence":"additional","affiliation":[{"name":"Department of Computer Science, NFC Institute of Engineering and Technology, Multan 60000, Pakistan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-1849-9679","authenticated-orcid":false,"given":"Syeda Nida","family":"Hassan","sequence":"additional","affiliation":[{"name":"Department of Business and Computing, Ravensbourne University, London SE10 0EW, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Daesik","family":"Jeong","sequence":"additional","affiliation":[{"name":"Division of Software Convergence, Sangmyung University, Seoul 03016, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7473-8441","authenticated-orcid":false,"given":"Rizwan Ali","family":"Naqvi","sequence":"additional","affiliation":[{"name":"Department of AI and Robotics, Sejong University, Seoul 05006, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,8,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"e1221","DOI":"10.1016\/S2214-109X(17)30393-5","article-title":"Global causes of blindness and distance vision impairment 1990\u20132020: A systematic review and meta-analysis","volume":"5","author":"Flaxman","year":"2017","journal-title":"Lancet Glob. Health"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1567","DOI":"10.18240\/ijo.2022.10.01","article-title":"LOVE your eyes\u2014World Sight Day 2022","volume":"15","author":"Wiedemann","year":"2022","journal-title":"Int. J. Ophthalmol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"e106","DOI":"10.1016\/S2214-109X(13)70145-1","article-title":"Global prevalence of age-related macular degeneration and disease burden projection for 2020 and 2040: A systematic review and meta-analysis","volume":"2","author":"Wong","year":"2014","journal-title":"Lancet Glob. Health"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"e14583","DOI":"10.1111\/dme.14583","article-title":"Incidence of sight-threatening diabetic retinopathy in an established urban screening programme: An 11-year cohort study","volume":"38","author":"Cheyne","year":"2021","journal-title":"Diabet. Med."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1792","DOI":"10.1016\/j.clinthera.2021.08.011","article-title":"Global burden of dry age-related macular degeneration: A targeted literature review","volume":"43","author":"Schultz","year":"2021","journal-title":"Clin. Ther."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1047","DOI":"10.2337\/diacare.27.5.1047","article-title":"Global prevalence of diabetes: Estimates for the year 2000 and projections for 2030","volume":"27","author":"Wild","year":"2004","journal-title":"Diabetes Care"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1186\/s40662-020-00182-7","article-title":"Different fundus imaging modalities and technical factors in AI screening for diabetic retinopathy: A review","volume":"7","author":"Lim","year":"2020","journal-title":"Eye Vis."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"He, J., Li, C., Ye, J., Qiao, Y., and Gu, L. (2021). Multi-label ocular disease classification with a dense correlation deep neural network. Biomed. Signal Process. Control., 63.","DOI":"10.1016\/j.bspc.2020.102167"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"212499","DOI":"10.1109\/ACCESS.2020.3040275","article-title":"Multi-label classification of fundus images with efficientnet","volume":"8","author":"Wang","year":"2020","journal-title":"IEEE Access"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1016\/j.media.2014.08.002","article-title":"Trainable COSFIRE filters for vessel delineation with application to retinal images","volume":"19","author":"Azzopardi","year":"2015","journal-title":"Med. Image Anal."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Srivastava, R., Liu, H., Chen, X., Duan, L., Kee Wong, D.W., and Liu, J. (2014). A survey on computer aided diagnosis for ocular diseases. BMC Med. Inform. Decis. Mak., 14.","DOI":"10.1186\/1472-6947-14-80"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"3338","DOI":"10.1109\/JBHI.2020.3012134","article-title":"Machine learning techniques for ophthalmic data processing: A review","volume":"24","author":"Sarhan","year":"2020","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1170","DOI":"10.1001\/jamaophthalmol.2017.3782","article-title":"Automated grading of age-related macular degeneration from color fundus images using deep convolutional neural networks","volume":"135","author":"Burlina","year":"2017","journal-title":"JAMA Ophthalmol."},{"key":"ref_14","unstructured":"Chen, X., Xu, Y., Duan, L., Yan, S., Zhang, Z., Wong, D.W.K., and Liu, J. (2014, January 1\u20135). Multiple ocular diseases classification with graph regularized probabilistic multi-label learning. Proceedings of the Computer Vision\u2014ACCV 2014: 12th Asian Conference on Computer Vision, Singapore. Revised Selected Papers, Part IV 12."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2509","DOI":"10.2337\/dc18-0147","article-title":"An automated grading system for detection of vision-threatening referable diabetic retinopathy on the basis of color fundus photographs","volume":"41","author":"Li","year":"2018","journal-title":"Diabetes Care"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1761","DOI":"10.1038\/s41593-019-0520-2","article-title":"A deep learning framework for neuroscience","volume":"22","author":"Richards","year":"2019","journal-title":"Nat. Neurosci."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Wang, Z., Keane, P.A., Chiang, M., Cheung, C.Y., Wong, T.Y., and Ting, D.S.W. (2022). Artificial intelligence and deep learning in ophthalmology. Artificial Intelligence in Medicine, Springer International Publishing.","DOI":"10.1007\/978-3-030-64573-1_200"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2402","DOI":"10.1001\/jama.2016.17216","article-title":"Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs","volume":"316","author":"Gulshan","year":"2016","journal-title":"JAMA"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1549","DOI":"10.1016\/j.jcmg.2019.06.009","article-title":"State-of-the-art deep learning in cardiovascular image analysis","volume":"12","author":"Litjens","year":"2019","journal-title":"JACC Cardiovasc. Imaging"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1038\/nature21056","article-title":"Dermatologist-level classification of skin cancer with deep neural networks","volume":"542","author":"Esteva","year":"2017","journal-title":"Nature"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"7344","DOI":"10.1038\/s41598-019-43708-3","article-title":"DEEPred: Automated protein function prediction with multi-task feed-forward deep neural networks","volume":"9","author":"Atalay","year":"2019","journal-title":"Sci. Rep."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"849","DOI":"10.1111\/jop.13042","article-title":"The use of artificial intelligence, machine learning and deep learning in oncologic histopathology","volume":"49","author":"Sultan","year":"2020","journal-title":"J. Oral Pathol. Med."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1186\/s12938-019-0649-y","article-title":"CNNs for automatic glaucoma assessment using fundus images: An extensive validation","volume":"18","author":"Morales","year":"2019","journal-title":"Biomed. Eng. Online"},{"key":"ref_24","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"4828","DOI":"10.1038\/s41467-021-25138-w","article-title":"Automatic detection of 39 fundus diseases and conditions in retinal photographs using deep neural networks","volume":"12","author":"Cen","year":"2021","journal-title":"Nat. Commun."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"14471","DOI":"10.1007\/s11042-022-13841-9","article-title":"Recent developments on computer aided systems for diagnosis of diabetic retinopathy: A review","volume":"82","author":"Dubey","year":"2023","journal-title":"Multimed. Tools Appl."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"397","DOI":"10.2174\/1573405615666190219102427","article-title":"Review of Various Tasks Performed in the Preprocessing Phase of a Diabetic Retinopathy Diagnosis System","volume":"16","author":"Ashraf","year":"2020","journal-title":"Curr. Med. Imaging"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"e0297052","DOI":"10.1371\/journal.pone.0297052","article-title":"Risk of major myopia-associated non-communicable ocular health disorders in Ghana","volume":"19","author":"Kyei","year":"2024","journal-title":"PLoS ONE"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"487","DOI":"10.1007\/s11831-023-09989-8","article-title":"Analysis of deep learning techniques for prediction of eye diseases: A systematic review","volume":"31","author":"Bali","year":"2024","journal-title":"Arch. Comput. Methods Eng."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Sharma, H., Wasim, J., and Sharma, P. (2024, January 21\u201323). Analysis of eye disease classification by fundus images using different machine\/deep\/transfer learning techniques. Proceedings of the 2024 4th International Conference on Innovative Practices in Technology and Management (ICIPTM), Noida, India.","DOI":"10.1109\/ICIPTM59628.2024.10563429"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1007\/s13755-024-00293-8","article-title":"A transfer learning enabled approach for ocular disease detection and classification","volume":"12","author":"Hassan","year":"2024","journal-title":"Health Inf. Sci. Syst."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1108\/IJICC-08-2023-0230","article-title":"Deep learning architectures in dental diagnostics: A systematic comparison of techniques for accurate prediction of dental disease through x-ray imaging","volume":"17","author":"Hasnain","year":"2024","journal-title":"Int. J. Intell. Comput. Cybern."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Tahir, A., Malik, H., and Chaudhry, M.U. (2024). Multi-classification Deep Learning Models for Detecting Multiple Chest Infection Using Cough and Breath Sounds. Deep Learning for Multimedia Processing Applications, CRC Press.","DOI":"10.1201\/9781003427674-12"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"63017","DOI":"10.1007\/s11042-023-18065-z","article-title":"Federated learning with deep convolutional neural networks for the detection of multiple chest diseases using chest X-rays","volume":"83","author":"Malik","year":"2024","journal-title":"Multimed. Tools Appl."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"e0296352","DOI":"10.1371\/journal.pone.0296352","article-title":"Multi-modal deep learning methods for classification of chest diseases using different medical imaging and cough sounds","volume":"19","author":"Malik","year":"2024","journal-title":"PLoS ONE"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"2434","DOI":"10.1038\/s41598-024-52131-2","article-title":"A concentrated machine learning-based classification system for age-related macular degeneration (AMD) diagnosis using fundus images","volume":"14","author":"Balaha","year":"2024","journal-title":"Sci. Rep."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"3115","DOI":"10.1038\/s41598-024-75867-3","article-title":"Optimising deep learning models for ophthalmological disorder classification","volume":"15","author":"Vidivelli","year":"2025","journal-title":"Sci. Rep."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"4125","DOI":"10.1038\/s41598-025-85372-w","article-title":"Fundus camera-based precision monitoring of blood vitamin A level for Wagyu cattle using deep learning","volume":"15","author":"Li","year":"2025","journal-title":"Sci. Rep."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1007\/s13755-020-00125-5","article-title":"Automated detection of mild and multi-class diabetic eye diseases using deep learning","volume":"8","author":"Sarki","year":"2020","journal-title":"Health Inf. Sci. Syst."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"212","DOI":"10.1016\/j.matpr.2022.01.466","article-title":"Binary classification of DR-diabetic retinopathy using CNN with fundus colour images","volume":"58","author":"Anoop","year":"2022","journal-title":"Mater. Today Proc."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"81","DOI":"10.4103\/meajo.meajo_406_20","article-title":"Validation of artificial intelligence algorithm in the detection and staging of diabetic retinopathy through fundus photography: An automated tool for detection and grading of diabetic retinopathy","volume":"28","author":"Pawar","year":"2021","journal-title":"Middle East Afr. J. Ophthalmol."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"38299","DOI":"10.1109\/ACCESS.2022.3165193","article-title":"Automatic severity classification of diabetic retinopathy based on densenet and convolutional block attention module","volume":"10","author":"Farag","year":"2022","journal-title":"IEEE Access"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"137881","DOI":"10.1109\/ACCESS.2023.3339574","article-title":"Enhancing Ocular Healthcare: Deep Learning-Based multi-class Diabetic Eye Disease Segmentation and Classification","volume":"11","author":"Vadduri","year":"2023","journal-title":"IEEE Access"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1186\/s40662-024-00384-3","article-title":"Prognostic potentials of AI in ophthalmology: Systemic disease forecasting via retinal imaging","volume":"11","author":"Tan","year":"2024","journal-title":"Eye Vis."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"10131","DOI":"10.1038\/s41598-021-89023-8","article-title":"Computer-aided autism diagnosis based on visual attention models using eye tracking","volume":"11","author":"Oliveira","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Li, B., Barney, E., Hudac, C., Nuechterlein, N., Ventola, P., Shapiro, L., and Shic, F. (2020, January 2\u20135). Selection of Eye-Tracking Stimuli for Prediction by Sparsely Grouped Input Variables for Neural Networks: Towards Biomarker Refinement for Autism. Proceedings of the ACM Symposium on Eye Tracking Research and Applications, Stuttgart, Germany.","DOI":"10.1145\/3379155.3391334"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1016\/j.ins.2018.01.051","article-title":"Deep convolution neural network for accurate diagnosis of glaucoma using digital fundus images","volume":"441","author":"Raghavendra","year":"2018","journal-title":"Inf. Sci."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"250","DOI":"10.1016\/j.eswa.2018.06.010","article-title":"Convolutional neural network and texture descriptor-based automatic detection and diagnosis of glaucoma","volume":"110","author":"Silva","year":"2018","journal-title":"Expert Syst. Appl."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Prananda, A.R., Frannita, E.L., Hutami, A.H.T., Maarif, M.R., Fitriyani, N.L., and Syafrudin, M. (2022). Retinal nerve fiber layer analysis using deep learning to improve glaucoma detection in eye disease assessment. Appl. Sci., 13.","DOI":"10.3390\/app13010037"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"12949","DOI":"10.1007\/s11042-024-19338-x","article-title":"Two stage-network: Automatic localization of Optic Disc (OD) and classification of glaucoma in fundus images using deep learning techniques","volume":"84","author":"Sheraz","year":"2024","journal-title":"Multimed. Tools Appl."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"1154","DOI":"10.1136\/bjo-2023-324012","article-title":"Evaluation of a self-imaging OCT for remote diagnosis and monitoring of retinal diseases","volume":"108","author":"Liu","year":"2024","journal-title":"Br. J. Ophthalmol."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"e0303094","DOI":"10.1371\/journal.pone.0303094","article-title":"A novel fusion of genetic grey wolf optimization and kernel extreme learning machines for precise diabetic eye disease classification","volume":"19","author":"Khan","year":"2024","journal-title":"PLoS ONE"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"399","DOI":"10.1016\/j.oret.2023.10.016","article-title":"Ocular and Systemic Risk Factors for Disease Worsening Among Patients with NPDR: Post Hoc Analysis of the PANORAMA Trial","volume":"8","author":"Wykoff","year":"2024","journal-title":"Ophthalmol. Retin."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Chelaramani, S., Gupta, M., Agarwal, V., Gupta, P., and Habash, R. (2021, January 5\u20139). Multi-task knowledge distillation for eye disease prediction. Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision (WACV), Virtual.","DOI":"10.1109\/WACV48630.2021.00403"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1016\/j.media.2019.02.011","article-title":"Deep-learning based multiclass retinal fluid segmentation and detection in optical coherence tomography images using a fully convolutional neural network","volume":"54","author":"Lu","year":"2019","journal-title":"Med. Image Anal."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"102130","DOI":"10.1016\/j.media.2021.102130","article-title":"A column-based deep learning method for the detection and quantification of atrophy associated with AMD in OCT scans","volume":"72","author":"Szeskin","year":"2021","journal-title":"Med. Image Anal."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Aamir, M., Irfan, M., Ali, T., Ali, G., Shaf, A., Al-Beshri, A., and Mahnashi, M.H. (2020). An adoptive threshold-based multi-level deep convolutional neural network for glaucoma eye disease detection and classification. Diagnostics, 10.","DOI":"10.3390\/diagnostics10080602"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"3244","DOI":"10.1364\/BOE.9.003244","article-title":"DRUNET: A dilated-residual U-Net deep learning network to segment optic nerve head tissues in optical coherence tomography images","volume":"9","author":"Devalla","year":"2018","journal-title":"Biomed. Opt. Express"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"108353","DOI":"10.1016\/j.engappai.2024.108353","article-title":"Multiscale triplet spatial information fusion-based deep learning method to detect retinal pigment signs with fundus images","volume":"133","author":"Arsalan","year":"2024","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"117968","DOI":"10.1016\/j.eswa.2022.117968","article-title":"Artificial Intelligence-based computer-aided diagnosis of glaucoma using retinal fundus images","volume":"207","author":"Haider","year":"2022","journal-title":"Expert Syst. Appl."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"109918","DOI":"10.1016\/j.asoc.2022.109918","article-title":"Exploring deep feature-blending capabilities to assist glaucoma screening","volume":"133","author":"Haider","year":"2023","journal-title":"Appl. Soft Comput."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"100931","DOI":"10.1016\/j.prime.2025.100931","article-title":"Glaucoma detection from retinal fundus images using graph convolution-based multi-task model","volume":"11","author":"Lenka","year":"2025","journal-title":"E-Prime-Adv. Electr. Eng. Electron. Energy"},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Hu, W., Li, K., Gagnon, J., Wang, Y., Raney, T., Chen, J., and Zhang, B. (2025). FundusNet: A Deep-Learning Approach for Fast Diagnosis of Neurodegenerative and Eye Diseases Using Fundus Images. Bioengineering, 12.","DOI":"10.3390\/bioengineering12010057"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"5157","DOI":"10.1038\/s41598-024-84922-y","article-title":"Multiple model visual feature embedding and selection method for an efficient ocular disease classification","volume":"15","author":"Kansal","year":"2025","journal-title":"Sci. Rep."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"e70063","DOI":"10.1002\/ima.70063","article-title":"MEDCnet: A Memory Efficient Approach for Processing High-Resolution Fundus Images for Diabetic Retinopathy Classification Using CNN","volume":"35","author":"Butt","year":"2025","journal-title":"Int. J. Imaging Syst. Technol."},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Nguyen, T.D., Le, D.T., Bum, J., Kim, S., Song, S.J., and Choo, H. (2024). Retinal disease diagnosis using deep learning on ultra-wide-field fundus images. Diagnostics, 14.","DOI":"10.3390\/diagnostics14010105"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"1246","DOI":"10.1038\/s41433-023-02914-0","article-title":"Predicting systemic diseases in fundus images: Systematic review of setting, reporting, bias, and models\u2019 clinical availability in deep learning studies","volume":"38","author":"Li","year":"2024","journal-title":"Eye"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"102059","DOI":"10.1016\/j.inffus.2023.102059","article-title":"Fundus-deepnet: Multi-label deep learning classification system for enhanced detection of multiple ocular diseases through data fusion of fundus images","volume":"102","author":"Zeebaree","year":"2024","journal-title":"Inf. Fusion"},{"key":"ref_69","first-page":"1344","article-title":"Automated Classification of Ophthalmic Disorders Using Color Fundus Images","volume":"12","author":"Hussain","year":"2024","journal-title":"Kurd. Stud."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1038\/s41746-023-00857-0","article-title":"A generalizable deep learning regression model for automated glaucoma screening from fundus images","volume":"6","author":"Hemelings","year":"2023","journal-title":"npj Digit. Med."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"10551","DOI":"10.1007\/s00521-023-08249-x","article-title":"EyeDeep-Net: A multi-class diagnosis of retinal diseases using deep neural network","volume":"35","author":"Sengar","year":"2023","journal-title":"Neural Comput. Appl."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"100140","DOI":"10.1016\/j.health.2023.100140","article-title":"A deep neural network and machine learning approach for retinal fundus image classification","volume":"3","author":"Thanki","year":"2023","journal-title":"Healthc. Anal."},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Nazir, T., Nawaz, M., Rashid, J., Mahum, R., Masood, M., Mehmood, A., and Hussain, A. (2021). Detection of diabetic eye disease from retinal images using a deep learning based CenterNet model. Sensors, 21.","DOI":"10.3390\/s21165283"},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"923","DOI":"10.1007\/s11760-020-01816-y","article-title":"Deep convolution feature aggregation: An application to diabetic retinopathy severity level prediction","volume":"15","author":"Bodapati","year":"2021","journal-title":"Signal Image Video Process."},{"key":"ref_75","unstructured":"Khan, M.S.M., Ahmed, M., Rasel, R.Z., and Khan, M.M. (2021, January 10\u201313). Cataract detection using convolutional neural network with VGG-19 model. Proceedings of the 2021 IEEE World AI IoT Congress (AIIoT), Online."},{"key":"ref_76","first-page":"e5","article-title":"Convolutional neural network for multi-class classification of diabetic eye disease","volume":"9","author":"Sarki","year":"2022","journal-title":"EAI Endorsed Trans. Scalable Inf. Syst."},{"key":"ref_77","doi-asserted-by":"crossref","unstructured":"Pahuja, R., Sisodia, U., Tiwari, A., Sharma, S., and Nagrath, P. (2022). A Dynamic Approach of Eye Disease Classification Using Deep Learning and Machine Learning Model. Proceedings of Data Analytics and Management, Springer.","DOI":"10.1007\/978-981-16-6289-8_59"},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"106094","DOI":"10.1016\/j.cmpb.2021.106094","article-title":"Diabetic retinopathy detection through convolutional neural networks with synaptic metaplasticity","volume":"206","year":"2021","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_79","doi-asserted-by":"crossref","unstructured":"Zhang, C., Lei, T., and Chen, P. (2022). Diabetic retinopathy grading by a source-free transfer learning approach. Biomed. Signal Process. Control., 73.","DOI":"10.1016\/j.bspc.2021.103423"},{"key":"ref_80","first-page":"679","article-title":"Diabetic retinopathy detection using transfer learning and deep learning","volume":"Volume 1","author":"Gangwar","year":"2021","journal-title":"Evolution in Computational Intelligence: Frontiers in Intelligent Computing: Theory and Applications (FICTA 2020)"},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"139367","DOI":"10.1109\/ACCESS.2020.3004766","article-title":"A comparison of transfer learning performance versus health experts in disease diagnosis from medical imaging","volume":"8","author":"Malik","year":"2020","journal-title":"IEEE Access"},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"13855","DOI":"10.1007\/s11042-022-13843-7","article-title":"CDC_Net: Multi-classification convolutional neural network model for detection of COVID-19, pneumothorax, pneumonia, lung Cancer, and tuberculosis using chest X-rays","volume":"82","author":"Malik","year":"2023","journal-title":"Multimed. Tools Appl."},{"key":"ref_83","first-page":"133","article-title":"Spatial Correlation Module for Classification of Multi-Label Ocular Diseases Using Color Fundus Images","volume":"76","author":"Khan","year":"2023","journal-title":"Comput. Mater. Contin."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"815","DOI":"10.1007\/s00530-021-00878-3","article-title":"BDCNet: Multi-classification convolutional neural network model for classification of COVID-19, pneumonia, and lung cancer from chest radiographs","volume":"28","author":"Malik","year":"2022","journal-title":"Multimed. Syst."},{"key":"ref_85","first-page":"82","article-title":"Deep learning-based classification of dental disease using X-rays","volume":"5","author":"Hasnain","year":"2023","journal-title":"J. Comput. Biomed. Inform."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"321","DOI":"10.1613\/jair.953","article-title":"SMOTE: Synthetic minority over-sampling technique","volume":"16","author":"Chawla","year":"2002","journal-title":"J. Artif. Intell. Res."},{"key":"ref_87","doi-asserted-by":"crossref","unstructured":"Riaz, S., Naeem, A., Malik, H., Naqvi, R.A., and Loh, W.K. (2023). Federated and Transfer Learning Methods for the Classification of Melanoma and Nonmelanoma Skin Cancers: A Prospective Study. Sensors, 23.","DOI":"10.3390\/s23208457"},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"38885","DOI":"10.1109\/ACCESS.2024.3374890","article-title":"NSGA-II-DL: Metaheuristic optimal feature selection with Deep Learning Framework for HER2 classification in Breast Cancer","volume":"12","author":"Majidpour","year":"2024","journal-title":"IEEE Access"},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1016\/j.csda.2003.10.021","article-title":"An anova test for functional data","volume":"47","author":"Cuevas","year":"2004","journal-title":"Comput. Stat. Data Anal."},{"key":"ref_90","first-page":"1","article-title":"Tukey\u2019s honestly significant difference (HSD) test","volume":"3","author":"Abdi","year":"2010","journal-title":"Encycl. Res. Des."},{"key":"ref_91","first-page":"258","article-title":"Improvement performance of the random forest method on unbalanced diabetes data classification using Smote-Tomek Link","volume":"7","author":"Hairani","year":"2023","journal-title":"JOIV Int. J. Inform. Vis."},{"key":"ref_92","first-page":"238","article-title":"Deep Learning-based Classification of COVID-19 Variants and Lung Cancer Using CT Scans","volume":"6","author":"Malik","year":"2023","journal-title":"J. Comput. Biomed. Inform."},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"106519","DOI":"10.1016\/j.compbiomed.2022.106519","article-title":"Discriminative kernel convolution network for multi-label ophthalmic disease detection on imbalanced fundus image dataset","volume":"153","author":"Bhati","year":"2023","journal-title":"Comput. Biol. Med."},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"103333","DOI":"10.1016\/j.inffus.2025.103333","article-title":"Fusion-Attention Diagnosis Network (FADNet): An end-to-end framework for optic disc segmentation and ocular disease classification","volume":"124","author":"Xiao","year":"2025","journal-title":"Inf. Fusion"},{"key":"ref_95","doi-asserted-by":"crossref","unstructured":"Hanfi, R., Mathur, H., and Shrivastava, R. (2025). Hybrid attention-based deep learning for multi-label ophthalmic disease detection on fundus images. Graefe\u2019s Arch. Clin. Exp. Ophthalmol., Online ahead of print.","DOI":"10.1007\/s00417-025-06858-x"},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"1065","DOI":"10.1007\/s42979-024-03451-7","article-title":"Eye Disease Prediction Using Deep Learning and Attention on Oct Scans","volume":"5","author":"Rani","year":"2024","journal-title":"SN Comput. Sci."},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"417","DOI":"10.1136\/bjo-2022-322183","article-title":"Ensemble of deep convolutional neural networks is more accurate and reliable than board-certified ophthalmologists at detecting multiple diseases in retinal fundus photographs","volume":"108","author":"Pandey","year":"2024","journal-title":"Br. J. Ophthalmol."}],"container-title":["Journal of Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2313-433X\/11\/8\/278\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T18:30:07Z","timestamp":1760034607000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2313-433X\/11\/8\/278"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,18]]},"references-count":97,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2025,8]]}},"alternative-id":["jimaging11080278"],"URL":"https:\/\/doi.org\/10.3390\/jimaging11080278","relation":{},"ISSN":["2313-433X"],"issn-type":[{"value":"2313-433X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,8,18]]}}}