Eye Diseases Classification Using Transfer Learning Technique of Deep Neural Network

The human eye is a complex organ susceptible to various diseases and abnormalities that can impair vision. Identifying retinal diseases early and precisely categorizing them is crucial to ensure effective treatment is administered promptly. In this proposed study, we implemented Convolutional Neural Networks (CNNs) along with pretrained neural network architectures. We use existing methods as the foundation and develop models tailored for detecting eye diseases, such as Diabetic Retinopathy (DR), Cataracts, and Glaucoma, specifically among diabetic patients. These models are applied for screening of retinal fundus images for accurate diagnosis. Prompt identification and suitable treatment can aid in preventing the commencement and advancement of diabetic retinopathy, cataracts, and glaucoma among patients. Accurate identification of these conditions is paramount in the healthcare sector. Our research aims to categorize eye diseases through the utilization of retinal fundus images for multiclass classification. The multiclass classification outcomes, implemented on the Kaggle platform, indicated that CNN, MobileNet, DenseNet121, ResNet and EfficientNetB3 yielded training accuracies of 98.50%, 99.94%, 99.37%, 99.90% and 99.97 as well as the validation accuracies of 88.22%, 95.67%, 93.99%, 93.30% and 95.97% respectively.