Multiple Lesions Detection of Fundus Images Based on Convolution Neural Network Algorithm With Improved SFLA
Weiping Ding, Ying Sun, Ren Longjie, Hengrong Ju, Zhihao Feng, Ming Li
Abstract
In order to effectively solve the problem of interlaced overlap in the fundus image lesions, large and small blood vessels packed densely and severely affected by light, and to achieve multi-label classification of fundus images. In this paper, a single population leapfrog optimization convolutional neural network algorithm (SFCNN) is proposed to detect and classify various fundus lesions. The algorithm uses the efficient search ability of the shuffled frog leaping algorithm to optimize the weight initialization and back propagation of the convolutional neural network. In order to deal with the problem of fundus image classification in the big data environment, the novel grouping optimization strategy is presented to effectively combine Spark platform and SFCNN algorithm to achieve large-scale fundus image classification and detection of multiple lesions. The experiment of the detection of fundus image lesions shows that the accuracy rate of SFCNN is better improved in both single lesion detection and overall detection, compared with other algorithms.