Transforming Ophthalmic Care: The Role of AI in Accurate Eye Disease Classification EDC

This research describes a unique strategy to classifying eye illnesses utilizing a Convolutional Neural Network (CNN) modification. The objective is to develop an automated system that accurately diagnoses and classifies eye diseases, leading to improved patient care and outcomes. A comprehensive dataset of eye images was collected from various sources and preprocessed to enhance quality and quantity. The proposed Eye Disease Classification (EDC) model was trained and optimized using well-known algorithms. The experimental findings illustrate the superiority of the suggested approach, achieving high precision ($95.63 \%$), recall (98.20%), F1-score (94.30%), and accuracy (94.50%), SVM, Decision Tree, KNN, and Random Forest are among the most often used classifiers, the results demonstrate the potential of the suggested technology to transform eye disease detection and therapy.