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

Diabetic Retinopathy (DR) is the prominent cause of blindness in the world. The early treatment of DR can be conducted from detection of microaneurysms (MAs) which is reddish spots in retinal images. An automated microaneurysm detection can be a helpful system for ophthalmologists for detecting of MA. In this paper, deep learning, in particular convolutional neural network (CNN), is used as a powerful tool to efficiently detect MAs from fundus images. Our method used a new technique utilising of a twostage 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 which is a competitive value with the state-of-the-art approaches.