{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T03:24:05Z","timestamp":1790047445755,"version":"4.0.1"},"reference-count":58,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2024,11,27]],"date-time":"2024-11-27T00:00:00Z","timestamp":1732665600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Background: Diabetic retinopathy, hypertensive retinopathy, glaucoma, and contrast-related eye diseases are well-recognized conditions resulting from high blood pressure, rising blood glucose, and elevated eye pressure. Later-stage symptoms usually include patches of cotton wool, restricted veins in the optic nerve, and buildup of blood in the optic nerve. Severe consequences include damage of the visual nerve, and retinal artery obstruction, and possible blindness may result from these conditions. An early illness diagnosis is made easier by the use of deep learning models and artificial intelligence (AI). Objectives: This study introduces a novel methodology called CAD-EYE for classifying diabetic retinopathy, hypertensive retinopathy, glaucoma, and contrast-related eye issues. Methods: The proposed system combines the features extracted using two deep learning (DL) models (MobileNet and EfficientNet) using feature fusion to increase the diagnostic system efficiency. The system uses fluorescence imaging for increasing accuracy as an image processing algorithm. The algorithm is added to increase the interpretability and explainability of the CAD-EYE system. This algorithm was not used in such an application in the previous literature to the best of the authors\u2019 knowledge. The study utilizes datasets sourced from reputable internet platforms to train the proposed system. Results: The system was trained on 65,871 fundus images from the collected datasets, achieving a 98% classification accuracy. A comparative analysis demonstrates that CAD-EYE surpasses cutting-edge models such as ResNet, GoogLeNet, VGGNet, InceptionV3, and Xception in terms of classification accuracy. A state-of-the-art comparison shows the superior performance of the model against previous work in the literature. Conclusions: These findings support the usefulness of CAD-EYE as a diagnosis tool that can help medical professionals diagnose an eye disease. However, this tool will not be replacing optometrists.<\/jats:p>","DOI":"10.3390\/diagnostics14232679","type":"journal-article","created":{"date-parts":[[2024,11,27]],"date-time":"2024-11-27T10:05:56Z","timestamp":1732701956000},"page":"2679","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["CAD-EYE: An Automated System for Multi-Eye Disease Classification Using Feature Fusion with Deep Learning Models and Fluorescence Imaging for Enhanced Interpretability"],"prefix":"10.3390","volume":"14","author":[{"given":"Maimoona","family":"Khalid","sequence":"first","affiliation":[{"name":"Department of Computer Software Engineering, Military College of Signals, National University of Science and Technology, Islamabad 44000, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-5794-7902","authenticated-orcid":false,"given":"Muhammad Zaheer","family":"Sajid","sequence":"additional","affiliation":[{"name":"Department of Computer Software Engineering, Military College of Signals, National University of Science and Technology, Islamabad 44000, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6145-4071","authenticated-orcid":false,"given":"Ayman","family":"Youssef","sequence":"additional","affiliation":[{"name":"Department of Computers and Systems, Electronics Research Institute, Cairo 11843, Egypt"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7940-1960","authenticated-orcid":false,"given":"Nauman Ali","family":"Khan","sequence":"additional","affiliation":[{"name":"Department of Computer Software Engineering, Military College of Signals, National University of Science and Technology, Islamabad 44000, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Muhammad Fareed","family":"Hamid","sequence":"additional","affiliation":[{"name":"Department of Computer Software Engineering, Military College of Signals, National University of Science and Technology, Islamabad 44000, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6850-5713","authenticated-orcid":false,"given":"Fakhar","family":"Abbas","sequence":"additional","affiliation":[{"name":"Centre for Trusted Internet and Community, National University of Singapore, Singapore 119077, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,11,27]]},"reference":[{"key":"ref_1","unstructured":"World Health Organization (2022, September 22). Blindness and Vision Impairment. Available online: https:\/\/www.who.int\/news-room\/fact-sheets\/detail\/blindness-and-visual-impairment."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"140","DOI":"10.1016\/S2213-8587(18)30128-1","article-title":"Incidence and progression of diabetic retinopathy: A systematic review","volume":"7","author":"Sabanayagam","year":"2019","journal-title":"Lancet Diabetes Endocrinol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"106452","DOI":"10.1016\/j.asoc.2020.106452","article-title":"Fuzzy based image edge detection algorithm for blood vessel detection in retinal images","volume":"94","author":"Orujov","year":"2020","journal-title":"Appl. Soft Comput."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1016\/j.diabres.2018.02.023","article-title":"IDF Diabetes Atlas: Global estimates of diabetes prevalence for 2017 and projections for 2045","volume":"138","author":"Cho","year":"2018","journal-title":"Diabetes Res. Clin. Pract."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Jain, A., Jalui, A., Jasani, J., Lahoti, Y., and Karani, R. (2019, January 25\u201326). Deep learning for detection and severity classification of diabetic retinopathy. Proceedings of the 2019 1st International Conference on Innovations in Information and Communication Technology (ICIICT), Chennai, India.","DOI":"10.1109\/ICIICT1.2019.8741456"},{"key":"ref_6","unstructured":"Walker, H.K., Hall, W.D., and Hurst, J.W. (1990). Clinical Methods: The History, Physical, and Laboratory Examinations, Butterworths."},{"key":"ref_7","unstructured":"Mishra, C., and Tripathy, K. (2024). Fundus Camera 2022 [Updated 25 August 2023]. StatePearls, StatePearls Publishing."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"231","DOI":"10.5566\/ias.1155","article-title":"Feedback on a publicly distributed image database: The Messidor database","volume":"33","author":"Zhang","year":"2014","journal-title":"Image Anal. Stereol."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Naveed, K., Abdullah, F., Madni, H.A., Khan, M.A., Khan, T.M., and Naqvi, S.S. (2021). Towards automated eye diagnosis: An improved retinal vessel segmentation framework using ensemble block matching 3D filter. Diagnostics, 11.","DOI":"10.3390\/diagnostics11010114"},{"key":"ref_10","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_11","doi-asserted-by":"crossref","first-page":"196","DOI":"10.1016\/j.irbm.2013.01.010","article-title":"TeleOphta: Machine learning and image processing methods for teleophthalmology","volume":"34","author":"Decenciere","year":"2013","journal-title":"Irbm"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"16012","DOI":"10.1038\/nrdp.2016.12","article-title":"Diabetic retinopathy","volume":"2","author":"Wong","year":"2016","journal-title":"Nat. Rev. Dis. Primers"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"649","DOI":"10.1001\/jama.2010.1111","article-title":"Prevalence of diabetic retinopathy in the United States, 2005\u20132008","volume":"304","author":"Zhang","year":"2010","journal-title":"JAMA"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1580","DOI":"10.1016\/j.ophtha.2021.04.027","article-title":"Global prevalence of diabetic retinopathy and projection of burden through 2045: Systematic review and meta-analysis","volume":"128","author":"Teo","year":"2021","journal-title":"Ophthalmology"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1038\/s41433-018-0269-y","article-title":"Fundus photograph-based deep learning algorithms in detecting diabetic retinopathy","volume":"33","author":"Raman","year":"2019","journal-title":"Eye"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"417","DOI":"10.1586\/eop.12.52","article-title":"Diabetic retinopathy management guidelines","volume":"7","author":"Chakrabarti","year":"2012","journal-title":"Expert Rev. Ophthalmol."},{"key":"ref_17","first-page":"1731","article-title":"Deep learning based convolutional neural network structured new image classification approach for eye disease identification","volume":"30","author":"Topaloglu","year":"2023","journal-title":"Sci. Iran."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Choudhary, A., Ahlawat, S., Urooj, S., Pathak, N., Lay-Ekuakille, A., and Sharma, N. (2023). A deep learning-based framework for retinal disease classification. Healthcare, 11.","DOI":"10.3390\/healthcare11020212"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Thomas, S., Hodge, W., and Malvankar-Mehta, M. (2015). The cost-effectiveness analysis of teleglaucoma screening device. PLoS ONE, 10.","DOI":"10.1371\/journal.pone.0137913"},{"key":"ref_20","first-page":"6509809","article-title":"Medical management of glaucoma in the 21st century from a Canadian perspective","volume":"2016","author":"Harasymowycz","year":"2016","journal-title":"J. Ophthalmol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"16067","DOI":"10.1038\/nrdp.2016.67","article-title":"Primary open-angle glaucoma","volume":"2","author":"Weinreb","year":"2016","journal-title":"Nat. Rev. Dis. Primers"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"2081","DOI":"10.1016\/j.ophtha.2014.05.013","article-title":"Global prevalence of glaucoma and projections of glaucoma burden through 2040: A systematic review and meta-analysis","volume":"121","author":"Tham","year":"2014","journal-title":"Ophthalmology"},{"key":"ref_23","first-page":"e11686","article-title":"Epidemiology of glaucoma: The past, present, and predictions for the future","volume":"12","author":"Allison","year":"2020","journal-title":"Cureus"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Akter, N., Fletcher, J., Perry, S., Simunovic, M.P., Briggs, N., and Roy, M. (2022). Glaucoma diagnosis using multi-feature analysis and a deep learning technique. Sci. Rep., 12.","DOI":"10.1038\/s41598-022-12147-y"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"598","DOI":"10.1016\/j.ajo.2007.12.018","article-title":"Role of optic nerve imaging in glaucoma clinical practice and clinical trials","volume":"145","author":"Greenfield","year":"2008","journal-title":"Am. J. Ophthalmol."},{"key":"ref_26","first-page":"CD008803","article-title":"Optic nerve head and fibre layer imaging for diagnosing glaucoma","volume":"11","author":"Michelessi","year":"2015","journal-title":"Cochrane Database Syst. Rev."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Ant\u00f3n L\u00f3pez, A., Nolivos, K., Pazos L\u00f3pez, M., Fatti, G., Ayala, M.E., Mart\u00ednez-Prats, E., Peral, O., Poposki, V., Tsiroukis, E., and Morilla-Grasa, A. (2021). Diagnostic accuracy and detection rate of glaucoma screening with optic disk photos, optical coherence tomography images, and telemedicine. J. Clin. Med., 11.","DOI":"10.3390\/jcm11010216"},{"key":"ref_28","first-page":"43","article-title":"Retinal fundus image for glaucoma detection: A review and study","volume":"28","author":"Kanse","year":"2019","journal-title":"J. Intell. Syst."},{"key":"ref_29","first-page":"100038","article-title":"Glaucoma detection in retinal fundus images using U-Net and supervised machine learning algorithms","volume":"5","author":"Shinde","year":"2021","journal-title":"Intell.-Based Med."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Yal\u00e7in, N., Alver, S., and Uluhatun, N. (2018, January 2\u20135). Classification of retinal images with deep learning for early detection of diabetic retinopathy disease. Proceedings of the 2018 26th Signal Processing and Communications Applications Conference (SIU), Izmir, Turkey.","DOI":"10.1109\/SIU.2018.8404369"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Chakrabarty, N. (2018, January 2\u20134). A deep learning method for the detection of diabetic retinopathy. Proceedings of the 2018 5th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON), Gorakhpur, India.","DOI":"10.1109\/UPCON.2018.8596839"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1136\/bjophthalmol-2018-313173","article-title":"Artificial intelligence and deep learning in ophthalmology","volume":"103","author":"Ting","year":"2019","journal-title":"Br. J. Ophthalmol."},{"key":"ref_33","unstructured":"Ramakrishnan, P., Sivagurunathan, P., and Sathish Kumar, N. (2019). Fruit classification based on convolutional neural network. Int. J. Control Autom."},{"key":"ref_34","unstructured":"Feng, X., Yang, J., Laine, A.F., and Angelini, E.D. (2017). Discriminative localization in CNNs for weakly-supervised segmentation of pulmonary nodules. Proceedings of the Medical Image Computing and Computer Assisted Intervention\u2014MICCAI 2017: 20th International Conference, Quebec City, QC, Canada, 11\u201313 September 2017, Proceedings, Part III 20, Springer."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"2493","DOI":"10.1109\/TMI.2018.2837012","article-title":"Disc-aware ensemble network for glaucoma screening from fundus image","volume":"37","author":"Fu","year":"2018","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Fumero, F., Alay\u00f3n, S., Sanchez, J.L., Sigut, J., and Gonzalez-Hernandez, M. (2011, January 27\u201330). RIM-ONE: An open retinal image database for optic nerve evaluation. Proceedings of the 2011 24th International Symposium on Computer-Based Medical Systems (CBMS), Bristol, UK.","DOI":"10.1109\/CBMS.2011.5999143"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Verma, K., Deep, P., and Ramakrishnan, A. (2011, January 16\u201318). Detection and classification of diabetic retinopathy using retinal images. Proceedings of the 2011 Annual IEEE India Conference, Hyderabad, India.","DOI":"10.1109\/INDCON.2011.6139346"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Gondal, W.M., K\u00f6hler, J.M., Grzeszick, R., Fink, G.A., and Hirsch, M. (2017, January 17\u201320). Weakly-supervised localization of diabetic retinopathy lesions in retinal fundus images. Proceedings of the 2017 IEEE International Conference on Image Processing (ICIP), Beijing, China.","DOI":"10.1109\/ICIP.2017.8296646"},{"key":"ref_39","unstructured":"Bock, R., Meier, J., Michelson, G., Ny\u00fal, L.G., and Hornegger, J. (2007). Classifying glaucoma with image-based features from fundus photographs. Proceedings of the Pattern Recognition: 29th DAGM Symposium, Heidelberg, Germany, 12\u201314 September 2007, Springer. Proceedings 29."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Malik, S., Kanwal, N., Asghar, M.N., Sadiq, M.A.A., Karamat, I., and Fleury, M. (2019). Data driven approach for eye disease classification with machine learning. Appl. Sci., 9.","DOI":"10.3390\/app9142789"},{"key":"ref_41","first-page":"41","article-title":"Glaucoma-deep: Detection of glaucoma eye disease on retinal fundus images using deep learning","volume":"8","author":"Abbas","year":"2017","journal-title":"Int. J. Adv. Comput. Sci. Appl."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Jain, L., Murthy, H.S., Patel, C., and Bansal, D. (2018, January 21\u201323). Retinal eye disease detection using deep learning. Proceedings of the 2018 Fourteenth International Conference on Information Processing (ICINPRO), Bengaluru, India.","DOI":"10.1109\/ICINPRO43533.2018.9096838"},{"key":"ref_43","first-page":"9","article-title":"Derin \u00d6\u011frenme Modellerini Kullanarak \u0130nsan Retinas\u0131n\u0131n Optik Koherans Tomografi G\u00f6r\u00fcnt\u00fclerinden Hastal\u0131k Tespiti","volume":"5","author":"Metin","year":"2022","journal-title":"Veri Bilimi"},{"key":"ref_44","first-page":"e5","article-title":"Convolutional neural network for multi-class classification of diabetic eye disease","volume":"9","author":"Sarki","year":"2021","journal-title":"EAI Endorsed Trans. Scalable Inf. Syst."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"e13232","DOI":"10.1111\/exsy.13232","article-title":"A deep feature fusion and selection-based retinal eye disease detection from oct images","volume":"40","author":"Umer","year":"2023","journal-title":"Expert Syst."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"962","DOI":"10.1016\/j.ophtha.2017.02.008","article-title":"Automated identification of diabetic retinopathy using deep learning","volume":"124","author":"Gargeya","year":"2017","journal-title":"Ophthalmology"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Sajid, M.Z., Hamid, M.F., Youssef, A., Yasmin, J., Perumal, G., Qureshi, I., Naqi, S.M., and Abbas, Q. (2023). DR-NASNet: Automated System to Detect and Classify Diabetic Retinopathy Severity Using Improved Pretrained NASNet Model. Diagnostics, 13.","DOI":"10.3390\/diagnostics13162645"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"14281","DOI":"10.1007\/s11042-023-15556-x","article-title":"FAS-Incept-HR: A fully automated system based on optimized inception model for hypertensive retinopathy classification","volume":"83","author":"Sajid","year":"2024","journal-title":"Multimed. Tools Appl."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Sajid, M.Z., Qureshi, I., Abbas, Q., Albathan, M., Shaheed, K., Youssef, A., Ferdous, S., and Hussain, A. (2023). Mobile-Hr: An ophthalmologic-based classification system for diagnosis of hypertensive retinopathy using optimized MobileNet architecture. Diagnostics, 13.","DOI":"10.3390\/diagnostics13081439"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"263","DOI":"10.1007\/BF01203680","article-title":"Cataract classification","volume":"88","author":"Hockwin","year":"1995","journal-title":"Doc. Ophthalmol."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Dong, Y., Zhang, Q., Qiao, Z., and Yang, J.J. (2017, January 18\u201320). Classification of cataract fundus image based on deep learning. Proceedings of the 2017 IEEE International Conference on Imaging Systems and Techniques (IST), Beijing, China.","DOI":"10.1109\/IST.2017.8261463"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"669","DOI":"10.1016\/j.survophthal.2023.02.007","article-title":"Artificial intelligence in uveitis: A comprehensive review","volume":"68","author":"Nakayama","year":"2023","journal-title":"Surv. Ophthalmol."},{"key":"ref_53","unstructured":"Kaggle (2024, August 15). Eyepacs, Aptos, Messidor Diabetic Retinopathy. Available online: https:\/\/www.kaggle.com\/datasets\/ascanipek\/eyepacs-aptos-messidor-diabetic-retinopathy."},{"key":"ref_54","unstructured":"(2024, August 15). Eye Disease Dataset. Available online: https:\/\/www.kaggle.com\/datasets\/kondwani\/eye-disease-dataset."},{"key":"ref_55","unstructured":"(2024, August 15). Dataset for Different Eye Disease. Available online: https:\/\/www.kaggle.com\/datasets\/dhirajmwagh1111\/dataset-for-different-eye-disease."},{"key":"ref_56","unstructured":"(2024, August 15). Eye Diseases Classification. Available online: https:\/\/www.kaggle.com\/datasets\/gunavenkatdoddi\/eye-diseases-classification."},{"key":"ref_57","unstructured":"(2024, August 15). Papers with Code\u2014DIARETDB1 Dataset, n.d. Available online: https:\/\/paperswithcode.com\/dataset\/diaretdb1."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Wahab Sait, A.R. (2023). Artificial Intelligence-Driven Eye Disease Classification Model. Appl. Sci., 13.","DOI":"10.3390\/app132011437"}],"container-title":["Diagnostics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-4418\/14\/23\/2679\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T16:40:53Z","timestamp":1760114453000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-4418\/14\/23\/2679"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,11,27]]},"references-count":58,"journal-issue":{"issue":"23","published-online":{"date-parts":[[2024,12]]}},"alternative-id":["diagnostics14232679"],"URL":"https:\/\/doi.org\/10.3390\/diagnostics14232679","relation":{},"ISSN":["2075-4418"],"issn-type":[{"value":"2075-4418","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,11,27]]}}}