Published Paper Details:

Diabetic Retinopathy (DR) is a leading cause of blindness in diabetic patients. Manual screening by ophthalmologists is time-consuming and labor-intensive. Deep Learning-based classification of DR stages from high-resolution fundus images using MobileNet and ResNet models offers a promising solution. The models were trained on the Kaggle dataset to enhance the efficiency and accuracy of DR diagnosis, detection, and prevention of vision loss. Deep Learning models detect and classify five DR stages (0-4) from fundus eye images with high accuracy. Input images are processed to extract features, which are then used to output the corresponding DR stage using an activation function. Utilizing Artificial Intelligence, the system analyzes large datasets and makes accurate predictions, reducing the workload of ophthalmologists and improving patient outcomes. This technology has the potential to revolutionize DR diagnosis, enabling timely and accurate detection, and preventing vision loss. Deep Learning in medical image analysis demonstrates effectiveness in improving healthcare outcomes, enabling enhanced patient care and treatment.