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

TL;DR

This paper presents a two-step CNN approach for detecting microaneurysms in fundus images, improving accuracy and reducing computational complexity for early diabetic retinopathy diagnosis.

Contribution

Introduces a novel two-stage training process for CNNs that enhances microaneurysm detection accuracy while lowering computational costs.

Findings

Achieved sensitivity of about 0.8 at >6 false positives per image

Validated on two public datasets with competitive results

Demonstrated efficiency of the two-stage training approach

Abstract

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.