%A Wen-she YIN,Jian-feng HE %T Detection method of hemorrhages of fundus image based on deep learning %0 Journal Article %D 2020 %J JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE) %R 10.6040/j.issn.1671-9352.0.2019.475 %P 62-71 %V 55 %N 9 %U {http://lxbwk.njournal.sdu.edu.cn/CN/abstract/article_3334.shtml} %8 2020-09-20 %X

This paper proposes a method for detecting the bleeding point of fundus images based on convolutional neural networks (CNN) plus conditional random fields (CRF). First, in order to avoid the influence of the background area of the image on subsequent detection, refer to the gray level information in the fundus image and adjust the image to the appropriate size according to the length of the fundus center to its edge, and then linearly weight the image to enhance its brightness and contrast; then, using the cropped image block to train the CNN model for detecting the bleeding point on the CNN architecture built on the VGG network; finally, to overcome the problems of false detection and missed detection in the bleeding point detection of the CNN model, CRF is used for CNN. The probability map of the model output is post-processed to achieve accurate detection of the bleeding point of the fundus image. The detection method proposed in this paper was trained and verified on the open Kaggle and Messidor databases, and achieved 98.8% accuracy, 99.4% recall rate and 99.1% F-score. In addition, the sensitivity tested on the DIARETDB1 database reached 98.5% and the F-score was 96.1%. The experimental results show that the effectiveness and superiority of the proposed method are illustrated from both image visual and quantitative detection.