Published Paper Details:

Diabetic retinopathy is a major cause of blindness worldwide, affecting millions of people. Early detection and treatment of diabetic retinopathy can prevent severe vision loss. In recent years, deep learning techniques, particularly Convolutional Neural Networks (CNNs), have demonstrated exceptional performance in various image recognition tasks, including medical image analysis. In this context, this project aims to develop a CNN-based model for detecting diabetic retinopathy in retinal images. The proposed approach involves collecting a dataset of retinal images labeled for the presence or absence of diabetic retinopathy, pre-processing the data, selecting a suitable CNN architecture, training the model on the data, and evaluating its performance. The anticipated outcome of this project is a CNN model that can accurately and reliably detect diabetic retinopathy in retinal images, which can assist in the early diagnosis and treatment of this severe condition.