Precision Detection of Diabetic Retinopathy via EfficientNetB3: A Deep Learning Approach

A main diabetes consequence, diabetic retinopathy (DR) causes blindness and vision problems. Effective therapy depends on early identification of DR phases and precise characterisation of them. Manual retinal image assessment is one of the time-consuming and error-prone traditional DR screening techniques. This work intends to enhance the accuracy and efficiency of DR diagnosis with EfficientNetB3 deep learning architecture. On a sizable collection of 294 retinal fundus images categorised into five DR stages—No_DR, Mild, Moderate, Severe, and Proliferative_DR—we optimised EfficientNetB3. With very good performance in spotting No_DR situations, the model obtained an overall accuracy of 84%. It struggled, though, to tell Mild, Moderate, and Proliferative_DR stages apart. The suggested approach calls for steps of data collecting, preprocessing, model training, and evaluation. Our findings show the possibilities of EfficientNetB3 for automated DR screening, providing a scalable approach to control the growing diabetic population and hence help early detection and treatment campaigns.