  In this work, we propose a novel microaneurysm (MA) detection for early
diabetic retinopathy screening using color fundus images. Since MA usually the
first lesions to appear as an 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 a 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 the pipeline of preprocessing, feature extraction,
classification followed by post processing is achieved. The presented method is
evaluated in publicly available Retinopathy Online Challenge (ROC) and
Diaretdb1v2 database and achieved state-of-the-art accuracy.
