Multiple Lesions Detection of Fundus Images Based on Convolution Neural Network Algorithm With Improved SFLA

Weiping Ding,Hengrong Ju,Ying Sun,Zhihao Feng,Longjie Ren,Ming Li

doi:10.1109/access.2020.2996569

Weiping Ding, Hengrong Ju + Show 4 more

Open Access

https://doi.org/10.1109/access.2020.2996569

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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.

Highlights

Color fundus images are the most basic way to diagnose eye diseases [1]
The colored retinal fundus images obtained from DRIVE (Digital Retinal Images for Vessel Extraction) Database and DIAREDB1(Diabetic Retinopathy Image Database) [29] are analyzed and examined to obtain the classification results of the proposed system
We first use the single population leapfrog algorithm to find the optimal initial weight, and the loss value calculated by the forward propagation of the convolutional neural network is monitored

Summary

INTRODUCTION

Color fundus images are the most basic way to diagnose eye diseases [1]. At the same time, Fundus image can make people discover various eye diseases as early as possible, such as glaucoma, optic neuritis, macular degeneration, etc. W. Ding et al.: Multiple Lesions Detection of Fundus Images Based on Convolution Neural Network Algorithm. This study proposes a single-popping leapfrog algorithm to optimize the weight initialization and backpropagation of convolutional neural networks. The algorithm uses the idea of group optimization based on Spark to train the data set in a distributed way, summarizes the weights of all the training and optimizes them by evolutionary leapfrog, and takes the optimal weights as the initial values of the group training By this way, the uncertainty of traditional average method can be avoided effectively, and the distributed training of convolutional neural network is more effective and reliable. CNN MODEL BASED ON IMPROVED SFLA In view of the complexity of fundus images, this study introduces a single-population frog hop into the convolutional neural network weight initialization and weight update.

WEIGHT INITIALIZATION BASED ON SINGLE-POPULATION LEAPFROG ALGORITHM

EXPERIMENTED RESULTS AND ANALYSIS

CONCLUSION