{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T01:59:38Z","timestamp":1783994378041,"version":"3.55.0"},"reference-count":44,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2025,10,4]],"date-time":"2025-10-04T00:00:00Z","timestamp":1759536000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.mdpi.com"],"crossmark-restriction":true},"short-container-title":["Sensors"],"abstract":"<jats:p>Retinal diseases are among the leading causes of vision impairment worldwide, and early detection is essential for enabling personalized treatments and preventing irreversible vision loss. In this paper, we propose a method aimed to identify and localize retinal conditions, i.e., Age-Related Macular Degeneration, Diabetic Retinopathy, and Choroidal Neovascularization, using explainable deep learning. For this purpose, we consider seven fine-tuned convolutional neural networks: MobileNet, LeNet, StandardCNN, CustomCNN, DenseNet, Inception, and EfficientNet. Moreover, we develop a novel architecture i.e., NeoNet, specifically designed for the detection of retinal diseases, achieving an accuracy of 99.5%. Furthermore, with the aim to provide explaianability behind the model decision, we highlight the most critical regions within retinal images influencing the predictions of the model. The obtained results show the ability of the model to detect pathological features, thereby supporting earlier and more accurate diagnosis of retinal diseases.<\/jats:p>","DOI":"10.3390\/s25196147","type":"journal-article","created":{"date-parts":[[2025,10,6]],"date-time":"2025-10-06T13:33:41Z","timestamp":1759757621000},"page":"6147","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["NeoNet: A Novel Deep Learning Model for Retinal Disease Diagnosis and Localization"],"prefix":"10.3390","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-0561-8609","authenticated-orcid":false,"given":"Valeria","family":"Sorgente","sequence":"first","affiliation":[{"name":"Department of Medicine and Health Sciences \u201cVincenzo Tiberio\u201d, University of Molise, 86100 Campobasso, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Simona","family":"Correra","sequence":"additional","affiliation":[{"name":"Department of Medicine and Health Sciences \u201cVincenzo Tiberio\u201d, University of Molise, 86100 Campobasso, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-1387-3015","authenticated-orcid":false,"given":"Ilenia","family":"Verrillo","sequence":"additional","affiliation":[{"name":"Department of Medicine and Health Sciences \u201cVincenzo Tiberio\u201d, University of Molise, 86100 Campobasso, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mario","family":"Cesarelli","sequence":"additional","affiliation":[{"name":"Department of Engineering, University of Sannio, 82100 Benevento, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fabio","family":"Martinelli","sequence":"additional","affiliation":[{"name":"Institute for High Performance Computing and Networking, National Research Council of Italy (CNR), 87036 Rende, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Antonella","family":"Santone","sequence":"additional","affiliation":[{"name":"Department of Medicine and Health Sciences \u201cVincenzo Tiberio\u201d, University of Molise, 86100 Campobasso, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9425-1657","authenticated-orcid":false,"given":"Francesco","family":"Mercaldo","sequence":"additional","affiliation":[{"name":"Department of Medicine and Health Sciences \u201cVincenzo Tiberio\u201d, University of Molise, 86100 Campobasso, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,10,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1016\/j.gendis.2021.02.009","article-title":"Age-related macular degeneration: Epidemiology, genetics, pathophysiology, diagnosis, and targeted therapy","volume":"9","author":"Deng","year":"2022","journal-title":"Genes Dis."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Tan, T.E., and Wong, T.Y. (2023). Diabetic retinopathy: Looking forward to 2030. Front. Endocrinol., 13.","DOI":"10.3389\/fendo.2022.1077669"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1394","DOI":"10.1167\/iovs.18-25534","article-title":"Prevalence and phenotypes of age-related macular degeneration in eyes with high myopia","volume":"60","author":"Corbelli","year":"2019","journal-title":"Investig. Ophthalmol. Vis. Sci."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1159\/000502387","article-title":"Strategies to tackle the global burden of diabetic retinopathy: From epidemiology to artificial intelligence","volume":"243","author":"Wong","year":"2020","journal-title":"Ophthalmologica"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"515","DOI":"10.1007\/s00592-017-0974-1","article-title":"The English national screening programme for diabetic retinopathy 2003\u20132016","volume":"54","author":"Scanlon","year":"2017","journal-title":"Acta Diabetol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"266","DOI":"10.1016\/j.neuron.2012.10.002","article-title":"The neuronal organization of the retina","volume":"76","author":"Masland","year":"2012","journal-title":"Neuron"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Leandro, I., Lorenzo, B., Aleksandar, M., Dario, M., Rosa, G., Agostino, A., and Daniele, T. (2023). OCT-based deep-learning models for the identification of retinal key signs. Sci. Rep., 13.","DOI":"10.1038\/s41598-023-41362-4"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Gencer, G., and Gencer, K. (2025). Advanced retinal disease detection from OCT images using a hybrid squeeze and excitation enhanced model. PLoS ONE, 20.","DOI":"10.1371\/journal.pone.0318657"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1038\/s41586-023-06555-x","article-title":"A foundation model for generalizable disease detection from retinal images","volume":"622","author":"Zhou","year":"2023","journal-title":"Nature"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"9","DOI":"10.17925\/USOR.2023.17.2.1","article-title":"Artificial Intelligence for the Diagnosis and Screening of Retinal Diseases","volume":"17","author":"Arrigo","year":"2023","journal-title":"TouchREVIEWS Ophthalmol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"101352","DOI":"10.1016\/j.preteyeres.2025.101352","article-title":"AI explainability in oculomics: How it works, its role in establishing trust, and what still needs to be addressed","volume":"106","author":"An","year":"2025","journal-title":"Prog. Retin. Eye Res."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1465369","DOI":"10.3389\/fmed.2024.1465369","article-title":"Generalizable and explainable artificial intelligence methods for retinal disease analysis: Challenges and future trends","volume":"11","author":"Chen","year":"2024","journal-title":"Front. Med."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Santone, A., Cesarelli, M., Colasuonno, E., Bevilacqua, V., and Mercaldo, F. (2024). A method for ocular disease diagnosis through visual prediction explainability. Electronics, 13.","DOI":"10.3390\/electronics13142706"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Fabre, M., Mateo, L., Lamaa, D., Baillif, S., Pag\u00e8s, G., Demange, L., Ronco, C., and Benhida, R. (2022). Recent advances in age-related macular degeneration therapies. Molecules, 27.","DOI":"10.3390\/molecules27165089"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"244","DOI":"10.1007\/s11892-011-0198-7","article-title":"AGEs, RAGE, and diabetic retinopathy","volume":"11","author":"Zong","year":"2011","journal-title":"Curr. Diabetes Rep."},{"key":"ref_16","first-page":"302","article-title":"Prevalence of diabetic retinopathy, proliferative diabetic retinopathy and non-proliferative diabetic retinopathy in Asian T2DM patients: A systematic review and meta-analysis","volume":"12","author":"Yang","year":"2019","journal-title":"Int. J. Ophthalmol."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"708","DOI":"10.1016\/j.bbe.2018.05.006","article-title":"Estimation of severity level of non-proliferative diabetic retinopathy for clinical aid","volume":"38","author":"Kaur","year":"2018","journal-title":"Biocybern. Biomed. Eng."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"101140","DOI":"10.1016\/j.disamonth.2021.101140","article-title":"Proliferative diabetic retinopathy (PDR)","volume":"67","author":"Chaudhary","year":"2021","journal-title":"Disease-a-Month"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1038\/s41746-018-0040-6","article-title":"Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices","volume":"1","author":"Lavin","year":"2018","journal-title":"NPJ Digit. Med."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2211","DOI":"10.1001\/jama.2017.18152","article-title":"Development and validation of a deep learning system for diabetic retinopathy and related eye diseases using retinal images from multiethnic populations with diabetes","volume":"318","author":"Ting","year":"2017","journal-title":"JAMA"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1016\/j.preteyeres.2007.07.005","article-title":"State-of-the-art retinal optical coherence tomography","volume":"27","author":"Drexler","year":"2008","journal-title":"Prog. Retin. Eye Res."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Ahn, S.J. (2025). Retinal Thickness Analysis Using Optical Coherence Tomography: Diagnostic and Monitoring Applications in Retinal Diseases. Diagnostics, 15.","DOI":"10.3390\/diagnostics15070833"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1038\/eye.2016.227","article-title":"A view of the current and future role of optical coherence tomography in the management of age-related macular degeneration","volume":"31","author":"Klimscha","year":"2017","journal-title":"Eye"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"20","DOI":"10.4103\/ijo.IJO_902_17","article-title":"Optical coherence tomography: A guide to interpretation of common macular diseases","volume":"66","author":"Bhende","year":"2018","journal-title":"Indian J. Ophthalmol."},{"key":"ref_25","unstructured":"Tempel, F., Groos, D., Ihlen, E.A.F., Adde, L., and Str\u00fcmke, I. (2024). Choose Your Explanation: A Comparison of SHAP and GradCAM in Human Activity Recognition. arXiv."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"105667","DOI":"10.1016\/j.imavis.2025.105667","article-title":"Explainable retinal disease classification and localization through Convolutional Neural Networks","volume":"162","author":"Santone","year":"2025","journal-title":"Image Vis. Comput."},{"key":"ref_27","first-page":"5","article-title":"Explainable Deep Learning for Breast Cancer Classification and Localization","volume":"6","author":"Vitulli","year":"2025","journal-title":"ACM Trans. Comput. Healthc."},{"key":"ref_28","unstructured":"Naren, O.S. (2025, September 28). Retinal OCT Image Classification\u2014C8 [Data Set]. Kaggle. Available online: https:\/\/www.kaggle.com\/datasets\/obulisainaren\/retinal-oct-c8."},{"key":"ref_29","unstructured":"Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., and Devin, M. (2025, September 28). TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Available online: https:\/\/www.tensorflow.org\/."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Islam, M.T., Imran, S.A., Arefeen, A., Hasan, M., and Shahnaz, C. (2019, January 28\u201330). Source and camera independent ophthalmic disease recognition from fundus image using neural network. Proceedings of the 2019 IEEE International Conference on Signal Processing, Information, Communication & Systems (SPICSCON), Dhaka, Bangladesh.","DOI":"10.1109\/SPICSCON48833.2019.9065162"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Vayadande, K., Ingale, V., Verma, V., Yeole, A., Zawar, S., and Jamadar, Z. (2022, January 26\u201327). Ocular disease recognition using deep learning. Proceedings of the 2022 International Conference on Signal and Information Processing (IConSIP), Pune, India.","DOI":"10.1109\/ICoNSIP49665.2022.10007470"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Bhati, A., Gour, N., Khanna, P., and Ojha, A. (2023). Discriminative kernel convolution network for multi-label ophthalmic disease detection on imbalanced fundus image dataset. Comput. Biol. Med., 153.","DOI":"10.1016\/j.compbiomed.2022.106519"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., and Batra, D. (2017, January 22\u201329). Grad-cam: Visual explanations from deep networks via gradient-based localization. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.74"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1016\/j.media.2019.01.012","article-title":"Attention gated networks: Learning to leverage salient regions in medical images","volume":"53","author":"Schlemper","year":"2019","journal-title":"Med Image Anal."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Wang, K., Xu, C., Li, G., Zhang, Y., Zheng, Y., and Sun, C. (2023). Combining convolutional neural networks and self-attention for fundus diseases identification. Sci. Rep., 13.","DOI":"10.1038\/s41598-022-27358-6"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"AIoa2300221","DOI":"10.1056\/AIoa2300221","article-title":"Development and validation of a multimodal multitask vision foundation model for generalist ophthalmic artificial intelligence","volume":"1","author":"Qiu","year":"2024","journal-title":"NEJM AI"},{"key":"ref_37","unstructured":"Shi, D., Zhang, W., Chen, X., Liu, Y., Yang, J., Huang, S., Tham, Y.C., Zheng, Y., and He, M. (2024). Eyefound: A multimodal generalist foundation model for ophthalmic imaging. arXiv."},{"key":"ref_38","unstructured":"Liu, Z., Xu, H., Woicik, A., Shapiro, L.G., Blazes, M., Wu, Y., Steffen, V., Cukras, C., Lee, C.S., and Zhang, M. (2024). OCTCube-M: A 3D multimodal optical coherence tomography foundation model for retinal and systemic diseases with cross-cohort and cross-device validation. arXiv."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Morano, J., Fazekas, B., S\u00fckei, E., Fecso, R., Emre, T., Gumpinger, M., Faustmann, G., Oghbaie, M., Schmidt-Erfurth, U., and Bogunovi\u0107, H. (2025). MIRAGE: Multimodal foundation model and benchmark for comprehensive retinal OCT image analysis. arXiv.","DOI":"10.1038\/s41746-025-01852-3"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1217","DOI":"10.1016\/j.ajhg.2021.05.004","article-title":"Large-scale machine-learning-based phenotyping significantly improves genomic discovery for optic nerve head morphology","volume":"108","author":"Alipanahi","year":"2021","journal-title":"Am. J. Hum. Genet."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1527","DOI":"10.1038\/s41588-024-01793-9","article-title":"Synthetic surrogates improve power for genome-wide association studies of partially missing phenotypes in population biobanks","volume":"56","author":"McCaw","year":"2024","journal-title":"Nat. Genet."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"1296","DOI":"10.1002\/jemt.23688","article-title":"Brain tumor detection and multi-classification using advanced deep learning techniques","volume":"84","author":"Sadad","year":"2021","journal-title":"Microsc. Res. Tech."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Jabeen, K., Khan, M.A., Alhaisoni, M., Tariq, U., Zhang, Y.D., Hamza, A., Mickus, A., and Dama\u0161evi\u010dius, R. (2022). Breast cancer classification from ultrasound images using probability-based optimal deep learning feature fusion. Sensors, 22.","DOI":"10.3390\/s22030807"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"38306","DOI":"10.1109\/ACCESS.2019.2906241","article-title":"Skin lesion classification using convolutional neural network with novel regularizer","volume":"7","author":"Albahar","year":"2019","journal-title":"IEEE Access"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/25\/19\/6147\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,8]],"date-time":"2025-10-08T04:10:27Z","timestamp":1759896627000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/25\/19\/6147"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,4]]},"references-count":44,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2025,10]]}},"alternative-id":["s25196147"],"URL":"https:\/\/doi.org\/10.3390\/s25196147","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,4]]}}}