A novel microaneurysms detection approach based on convolutional neural networks with reinforcement sample learning algorithm

• Published in: Health information science and systems Vol. 5; no. 1; p. 14
• Main Authors: Budak, Umit, Şengür, Abdulkadir, Guo, Yanhui, Akbulut, Yaman
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.11.2017; BioMed Central Ltd
• Subjects: Aneurysm; Artificial neural networks; Bioinformatics; Computational Biology/Bioinformatics; Computer Science; Diabetic retinopathy; Diagnosis; Health Informatics; Information Systems and Communication Service; Special Issue on Artificial Intelligence in Health and Medicine; Diabetic retinopathy; Color fundus images; Deep convolutional neural network; Microaneurysms detection; Reinforcement sample learning strategy
• ISSN: 2047-2501; 2047-2501
• DOI: 10.1007/s13755-017-0034-9

Microaneurysms (MAs) are known as early signs of diabetic-retinopathy which are called red lesions in color fundus images. Detection of MAs in fundus images needs highly skilled physicians or eye angiography. Eye angiography is an invasive and expensive procedure. Therefore, an automatic detection system to identify the MAs locations in fundus images is in demand. In this paper, we proposed a system to detect the MAs in colored fundus images. The proposed method composed of three stages. In the first stage, a series of pre-processing steps are used to make the input images more convenient for MAs detection. To this end, green channel decomposition, Gaussian filtering, median filtering, back ground determination, and subtraction operations are applied to input colored fundus images. After pre-processing, a candidate MAs extraction procedure is applied to detect potential regions. A five-stepped procedure is adopted to get the potential MA locations. Finally, deep convolutional neural network (DCNN) with reinforcement sample learning strategy is used to train the proposed system. The DCNN is trained with color image patches which are collected from ground-truth MA locations and non-MA locations. We conducted extensive experiments on ROC dataset to evaluate of our proposal. The results are encouraging.