Eye diseases are a significant health concern that adversely impacts human life. Cataracts,diabetic retinopathy, and glaucoma are some of the diseases that cause irreversible and serioushealth problems. Eye health is greatly influenced by age, genetics, and environmental factors.Proper diagnosis of eye ailments is crucial, as it ensures accurate and effective treatment. Theproximity of disease detection to error for accurate and personalized treatment intensifies theclinician's responsibility further. Developing technology and deep learning make it feasible todetermine if an individual has an eye disease, and to identify the specific disease. The objective ofthis research is to design resolutions for detecting significant health issues such as eye diseaseswith the aid of deep learning models. DenseNet, EfficientNet, Xception, VGG, and ResNetarchitectures, which are prominent Convolutional Neural Network models, are utilized to addressthe issue at hand. Technical term abbreviations are explained where first used. The dataset7employed for detecting diseases in retinal fundus images consists of a total of 4217 images,comprising 1038 cataracts, 1098 diabetic-retinopathy, 1007 glaucoma, and 1074 healthyindividuals. The performance of the tested models was assessed using evaluation metrics such asaccuracy, recall, precision, F1-score, and Matthews's correlation coefficient metrics through 10-fold cross-validation. Upon analysis of the classification performances, the EfficientNet modelobtained the best results for these evaluation metrics at 87.84%, 92.84%, 94.41%, 93.53%, and83.87%, respectively. Thus, EfficientNet architecture delivered the best classification performancein this context
