000136069 001__ 136069
000136069 005__ 20250908131420.0
000136069 0247_ $$2doi$$a10.12688/f1000research.122288.2
000136069 0248_ $$2sideral$$a139062
000136069 037__ $$aART-2024-139062
000136069 041__ $$aeng
000136069 100__ $$aArias-Serrano, Isaac
000136069 245__ $$aArtificial intelligence based glaucoma and diabetic retinopathy detection using MATLAB — retrained AlexNet convolutional neural network
000136069 260__ $$c2024
000136069 5060_ $$aAccess copy available to the general public$$fUnrestricted
000136069 5203_ $$aGlaucoma and diabetic retinopathy (DR) are the leading causes of irreversible retinal damage leading to blindness. Early detection of these diseases through regular screening is especially important to prevent progression. Retinal fundus imaging serves as the principal method for diagnosing glaucoma and DR. Consequently, automated detection of eye diseases represents a significant application of retinal image analysis. Compared with classical diagnostic techniques, image classification by convolutional neural networks (CNN) exhibits potential for effective eye disease detection. Methods This paper proposes the use of MATLAB – retrained AlexNet CNN for computerized eye diseases identification, particularly glaucoma and diabetic retinopathy, by employing retinal fundus images. The acquisition of the database was carried out through free access databases and access upon request. A transfer learning technique was employed to retrain the AlexNet CNN for non-disease (Non_D), glaucoma (Sus_G) and diabetic retinopathy (Sus_R) classification. Moreover, model benchmarking was conducted using ResNet50 and GoogLeNet architectures. A Grad-CAM analysis is also incorporated for each eye condition examined. Results Metrics for validation accuracy, false positives, false negatives, precision, and recall were reported. Validation accuracies for the NetTransfer (I-V) and netAlexNet ranged from 89.7% to 94.3%, demonstrating varied effectiveness in identifying Non_D, Sus_G, and Sus_R categories, with netAlexNet achieving a 93.2% accuracy in the benchmarking of models against netResNet50 at 93.8% and netGoogLeNet at 90.4%. Conclusions This study demonstrates the efficacy of using a MATLAB-retrained AlexNet CNN for detecting glaucoma and diabetic retinopathy. It emphasizes the need for automated early detection tools, proposing CNNs as accessible solutions without replacing existing technologies.
000136069 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000136069 592__ $$a0.537$$b2024
000136069 593__ $$aArts and Humanities (miscellaneous)$$c2024$$dQ1
000136069 593__ $$aImmunology and Microbiology (miscellaneous)$$c2024$$dQ2
000136069 593__ $$aSocial Sciences (miscellaneous)$$c2024$$dQ2
000136069 593__ $$aMedicine (miscellaneous)$$c2024$$dQ2
000136069 593__ $$aPharmacology, Toxicology and Pharmaceutics (miscellaneous)$$c2024$$dQ2
000136069 593__ $$aBiochemistry, Genetics and Molecular Biology (miscellaneous)$$c2024$$dQ2
000136069 593__ $$aLibrary and Information Sciences$$c2024$$dQ2
000136069 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000136069 700__ $$aVelásquez-López, Paolo A.
000136069 700__ $$aAvila-Briones, Laura N.
000136069 700__ $$aLaurido-Mora, Fanny C.
000136069 700__ $$aVillalba-Meneses, Fernando
000136069 700__ $$aTirado-Espin, Andrés
000136069 700__ $$aCruz-Varela, Jonathan
000136069 700__ $$aAlmeida-Galárraga, Diego
000136069 773__ $$g12, 14 (2024), 1-30$$tF1000Research$$x2046-1402
000136069 8564_ $$s3696519$$uhttps://zaguan.unizar.es/record/136069/files/texto_completo.pdf$$yVersión publicada
000136069 8564_ $$s1832324$$uhttps://zaguan.unizar.es/record/136069/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000136069 909CO $$ooai:zaguan.unizar.es:136069$$particulos$$pdriver
000136069 951__ $$a2025-09-08-12:52:57
000136069 980__ $$aARTICLE