The region of interest localization for glaucoma analysis from retinal fundus image using deep learning

Anirban Mitra, Priya Shankar Banerjee, Sudipta Roy, Somasis Roy, Sanjit Kumar Setua

Abstract: Highlights•Retinal fundus image analysis without manual intervention has been rising as an imperative analytical approach for early detection.•A Convolution Neural Network (CNN) has trained on full images to predict bounding boxes along with their analogous probabilities and confidence scores.•The following projected method accomplish an accuracy of 99.05% and 98.78% on the Kaggle and MESSIDOR test sets for ROI detection.•Proposed methodology indicates that proposed network is able to perceive ROI in fundus images in 0.0045 s at 25 ms of latency.•Proposed technique has better diagnosis of eye diseases in the coming future in a faster and a much more reliable way.