{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,14]],"date-time":"2026-09-14T15:04:48Z","timestamp":1789398288716,"version":"build-2803163510"},"reference-count":30,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2025,4,2]],"date-time":"2025-04-02T00:00:00Z","timestamp":1743552000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Instituto Polit\u00e9cnico Nacional"},{"name":"SNII"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Background: Ocular diseases have been a severe problem worldwide, specifically in underdeveloped countries that do not have enough technology or economy to treat them. It would be beneficial to have software with low installation complexity and ease of use, allowing high efficacy in diagnosing eye diseases. This study aims to design and implement an algorithm based on deep learning to classify ocular diseases with high precision. Methods: This work describes digital image processing techniques for the easier handling of eye images; in particular, blur filters were used. The Canny filter was also applied to obtain the edges that allow the difference between the analyzed diseases. Once the images were pre-processed, a convolutional neural network of our own design was applied to perform the classification task. The validation algorithm used in this work was the hold-out algorithm (80\u201320). The metrics used to evaluate our proposal were the confusion matrix, accuracy, recall precision, and F1-score. Results: The dataset has five classes, namely, normal, cataract, diabetic retinopathy, glaucoma, and other retina diseases. The network architecture consists of 11 layers, including three convolutional layers, three max pooling layers, one batch normalization layer, one flattening layer, two hidden layers, and one output layer. This model resulted in 97% efficiency across all metrics. Conclusions: With the individual analysis of each metric, it can be observed that the proposed algorithm is capable of differentiating, first, images of healthy eyes from diseased ones and, second, adequately classifying eye diseases.<\/jats:p>","DOI":"10.3390\/diagnostics15070916","type":"journal-article","created":{"date-parts":[[2025,4,2]],"date-time":"2025-04-02T16:26:31Z","timestamp":1743611191000},"page":"916","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Identification of Eye Diseases Through Deep Learning"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8179-0398","authenticated-orcid":false,"given":"Elena","family":"Acevedo","sequence":"first","affiliation":[{"name":"Instituto Polit\u00e9cnico Nacional, Escuela Superior de Ingenier\u00eda Mec\u00e1nica y El\u00e9ctrica, Campus Zacatenco, Mexico City 07700, Mexico"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dinora","family":"Orantes","sequence":"additional","affiliation":[{"name":"Instituto Polit\u00e9cnico Nacional, Centro de Investigaci\u00f3n en Computaci\u00f3n, Mexico City 07700, Mexico"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marco","family":"Acevedo","sequence":"additional","affiliation":[{"name":"Instituto Polit\u00e9cnico Nacional, Escuela Superior de Ingenier\u00eda Mec\u00e1nica y El\u00e9ctrica, Campus Zacatenco, Mexico City 07700, Mexico"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ricardo","family":"Carre\u00f1o","sequence":"additional","affiliation":[{"name":"Department of Systems Engineering, Universidad del Istmo, Campus Tehuantepec, Ciudad Universitaria S\/N, Barrio Santa Cruz, 4a. Secci\u00f3n Sto. Domingo Tehuantepec, Oaxaca 70760, Mexico"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,4,2]]},"reference":[{"key":"ref_1","unstructured":"UNAM Global (2023). En M\u00e9xico, Cerca de 1.5 Millones de Personas Tienen Glaucoma, CDMX, Ciudad Universitaria. Bolet\u00edn: N\u00famero 221."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1901","DOI":"10.1001\/jama.2014.3192","article-title":"The Pathophysiology and Treatment of Glaucoma","volume":"311","author":"Weinreb","year":"2014","journal-title":"J. Am. Med. Assoc."},{"key":"ref_3","first-page":"23","article-title":"Retinopat\u00eda diab\u00e9tica","volume":"31","author":"Aliseda","year":"2008","journal-title":"An. Sis. San. Navar."},{"key":"ref_4","unstructured":"(2025, March 27). American Academy of Ophthalmology. 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