Improved Microaneurysm Detection using Deep Neural Networks

In this work, we propose a novel microaneurysm (MA) detection for early dieabetic ratinopathy screening using color fundus images. Since MA usually the first lesions to appear as a indicator of diabetic retinopathy, accurate detection of MA is necessary for treatment. Each pixel of the image is classified as either MA or non-MA using deep neural network with dropout training procedure using maxout activation function. No preprocessing step or manual feature extraction is required. Substantial improvements over standard MA detection method based on pipeline of preprocessing, feature extraction, classification followed by postprocessing is achieved. The presented method is evaluated in publicly available Retinopathy Online Challenge (ROC) and Diaretdb1v2 database and achieved state-of-the-art accuracy.