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Diabetic Retinopathy (DR) is a prominent cause of blindness in the world. Theearly treatment of DR can be conducted from detection of microaneurysms (MAs)which appears as reddish spots in retinal images. An automated microaneurysmdetection can be a helpful system for ophthalmologists. In this paper, deeplearning, in particular convolutional neural network (CNN), is used as apowerful tool to efficiently detect MAs from fundus images. In our method a newtechnique is used to utilise a two-stage training process which results in anaccurate detection, while decreasing computational complexity in comparisonwith previous works. To validate our proposed method, an experiment isconducted using Keras library to implement our proposed CNN on two standardpublicly available datasets. Our results show a promising sensitivity value ofabout 0.8 at the average number of false positive per image greater than 6which is a competitive value with the state-of-the-art approaches.

Microaneurysm Detection in Fundus Images Using a Two-step Convolutional Neural Networks