Eye Disease Classification Using Deep Learning Techniques
This work addresses early diagnosis of eye diseases to prevent vision loss, but it is incremental as it applies existing deep learning methods to a specific medical domain.
The study tackled the problem of classifying eye diseases by using convolutional neural networks and transfer learning to distinguish between normal eyes and those with diabetic retinopathy, cataract, or glaucoma, achieving 94% accuracy with transfer learning compared to 84% with a traditional CNN.
Eye is the essential sense organ for vision function. Due to the fact that certain eye disorders might result in vision loss, it is essential to diagnose and treat eye diseases early on. By identifying common eye illnesses and performing an eye check, eye care providers can safeguard patients against vision loss or blindness. Convolutional neural networks (CNN) and transfer learning were employed in this study to discriminate between a normal eye and one with diabetic retinopathy, cataract, or glaucoma disease. Using transfer learning for multi-class classification, high accuracy was achieved at 94% while the traditional CNN achieved 84% rate.