Microaneurysm Detection in Fundus Images Using a Two-step Convolutional Neural Networks
This work addresses diabetic retinopathy diagnosis for ophthalmologists by providing an automated detection tool, but it is incremental as it builds on existing deep learning approaches with a two-stage training process.
The paper tackled automated detection of microaneurysms in fundus images for diabetic retinopathy screening, achieving a sensitivity of about 0.8 with over 6 false positives per image, which is competitive with state-of-the-art methods.
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.