Automated Multiclass Classification for Ocular Disease Diagnosis Using Deep Learning

Blindness, largely resulting from conditions such as Diabetic Retinopathy, Glaucoma, and Cataract, stands as a significant health concern. This paper introduces a novel approach proposing an automatic, self-diagnosing method utilizing a deep learning model for the rapid detection of these three prevalent eye diseases, achieving a high prediction rate within a minute. Leveraging revolutionary deep learning techniques, our model features a convolutional architecture with specifically designed layers to address the challenge of classifying multiple ocular diseases. Consequently, the proposed model yields predictions based on fundus images, effectively indicating the existence or non-existence of Diabetic Retinopathy, Glaucoma, or Cataract.