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