Retina radar-diabetic retinopathy disease detection using TensorFlow

DR stands as the primary diabetes-related reason behind vision disability and loss at a global scale while targeting diabetes patients specifically. Prompt treatment, along with early diagnosis of diabetic complications, reduces the risk of severe sight loss. Currently, standard diagnostic procedures rely mostly on manual ophthalmological examinations, which are time-consuming and yield variable findings. In this study, we provide Retina Radar, an automated DR detection system driven by TensorFlow Deep Learning. Diabetic Retinopathy (DR) is diagnosed at various stages using a convolutional neural network (CNNs) trained on fundus pictures taken from the retina. To train the algorithm, pictures of previously categorized retinas were used from the Kaggle EyePACS dataset. The model improved in picture preprocessing due to augmentation and normalization approaches, which increased its flexibility and generalizability. We increased our scope y accuracy while maintaining efficiency during the training process by using TensorFlow high level APIs and smart hyperparameter optimization. Accuracy, precision, and recall were tested, and performance was evaluated using the F1 score and the Area Under the Receiver Operating Characteristic Curve (AUC-ROC). The system achieved an accuracy of X% and a performance measurement of Y% using AUC-ROC with higher speed and accuracy outcomes. Grad-CAM creates heatmaps as part of the model to show significant picture areas in retinal scans, improving the network's clinical transparency and interpretability. These results show specifically that diabetic retinopathy detection and diagnosis can be substantially improved using deep learning models. In particular, Retina Radar offers a scalable and low-skill solution for early screening and requires no specialized medical personnel — making it especially suited to resource-limited settings. We plan to expand the dataset to incorporate diversity of populations and integrate the system into real-world clinical workflow for validation purposes in future work.