An Intelligent Diabetic Retinopathy Forecasting System using Modified Deep Neural Network

Diabetic Retinopathy (DR) is a severe complication of diabetes that can lead to vision impairment and blindness if not detected and treated early. In this research, we propose an advanced forecasting system leveraging a Modified Deep Neural Network (DNN) to enhance the accuracy and efficiency of DR diagnosis. The proposed system integrates a deep learning framework with modifications tailored to the unique characteristics of retinal images affected by diabetes. We introduce specialized features extraction techniques and optimize the network architecture to accommodate the intricacies of diabetic retinal pathology. A comprehensive dataset comprising diverse retinal images is utilized for training and validating the modified DNN model. The experimental results demonstrate superior forecasting accuracy compared to existing methods, highlighting the effectiveness of the proposed approach in early detection and prognosis of diabetic retinopathy. This intelligent forecasting system holds significant promise for improving the clinical management of diabetic patients by facilitating timely intervention and reducing the risk of irreversible visual impairment.