MicroNet: microaneurysm detection in retinal fundus images using convolutional neural network

Diabetic retinopathy (DR) is a complication of diabetes mellitus that leads to vision loss if not treated timely. Microaneurysms (MA) is the first clinical sign of DR. An automatic MA detection method through retinal fundus images has been proposed in this paper. The MA detection method consists of three steps: preprocessing, MA candidate detection, and pixel-wise classification. A novel convolutional neural network (CNN) architecture has been proposed in this paper to train MA and non-MA patches, and the majority voting technique has been used to detect the MA patches. The presented method has been evaluated using an openly available dataset, namely Retinopathy Online Challenge (ROC). The proposed method produces 92% of area under the receiver operating characteristics (AUC) curve, which is better than other state-of-the-art methods. The developed CNN architecture produced the highest accuracy even for fewer images, which helps effective DR screening.