Pathologies Detection in Retinal Images

The main goal of this work is to design and implement an algorithm for the detection of microaneurysms, hard exudates, and soft exudates on color fundus images. An algorithm for detecting objects based on deep learning has been proposed. The Faster R-CNN architecture with a feature pyramid network and a pre-pretrained residual network was used together with various data transformation methods. A total of six retinal image datasets were used to train, validate and test the models. The trained models achieved 0.46 mean average accuracy (mAP) in microaneurysm detection and 0.48 mAP in exudates detection during testing. The resulting models have been compared with published articles and make it possible to detect given pathologies with commendable accuracy