Multiple Lesions Detection of Fundus Images Based on CNN Algorithm Optimized by Single Population Frog-Leaping Algorithm - REN Longjie, SUN Ying, DING Weiping, JU Hengrong, CAO Jinxin (Jisuanji kexue yu tansuo, 2021)
In order to effectively solve the problem of interlaced overlap in the fundus image lesions, large and small blood vessels and severely affected by light, and to achieve multi-label classification of fundus images, in this paper, a single population frog-leaping optimization convolutional neural network algorithm (SFCNN) is proposed to detect various fundus lesions. The algorithm retains the efficient searching ability of the shuffled frog leaping algorithm (SFLA). It is simplified into a single population frog-leaping algorithm and effectively combined with the traditional convolutional neural networks (CNN). When initializing the network, the initial weight of the network is optimized by the frog-leaping algorithm. In the process of network iteration, the forward propagation loss of convolutional neural network is monitored and the abnormal weight is corrected by using the optimization ability of frog-leaping algorithm. After the network meets the end conditions, the final weight is optimized by frog-leaping, which further optimizes the network weight, so as to realize the detection and classification of complex fundus image with multiple lesions. The experiment of the detection of fundus image lesions shows that compared with CNN algorithm, the accuracy of the proposed algorithm is improved in both single lesion detection and overall detection.