Retinal eye disease detection using deep learning

The global prevalence of retinal abnormalities affects millions of individuals, highlighting the urgency of early detection and intervention to prevent the advancement of these conditions, ultimately mitigating the risk of avoidable blindness. In this thesis, we delve into the critical realm of retinal disease detection using deep learning techniques. Leveraging a diverse ensemble of state-of-the-art neural network architectures, including MobileNetV2, ResNet50, InceptionV3, and DenseNet, we conduct a comprehensive evaluation of their performance in classifying retinal scans. Our meticulous preprocessing steps, resize images, convert grayscale to RGB and rigorous training cycles lay the foundation for an advanced model. Notably, our results reveal that ResNet50 outperforms other models, achieving an accuracy of 0.89, setting a new benchmark in retinal scan analysis. This research contributes to the vital field of early retinal disease detection, offering the potential to enhance clinical diagnosis and patient outcomes