Convolutional neural network weight optimization method for retinal lesion classification

Abstract

The invention relates to the field of medical information intelligent processing, in particular to a convolutional neural network weight optimization method for retinal lesion classification. The method comprises the following steps: firstly, acquiring a fundus image training set and a corresponding multi-lesion label; searching an optimal initial weight value through a single-population leapfrogalgorithm, then constructing a convolution layer, a pooling layer and a full connection layer in the convolutional neural network, and taking the optimal initial weight value as a parameter of first forward propagation calculation; performing cross entropy loss calculation and summation on the four predicted values of the four lesions in the retina and the true value to obtain a loss value, judging whether the loss value is abnormal or not, if the loss value is abnormal, generating a frog group around the weight of the previous forward propagation, and searching an optimal frog updating network weight; otherwise, updating the network weight by adopting a gradient descent algorithm; and finally, optimizing the final weight. According to the method, the accuracy of fundus image multi-lesiondetection can be effectively improved, and the method has high application value for retinal diseases and adjuvant therapy.

Description

technical field [0001] The invention relates to the field of intelligent processing of medical information, in particular to a convolutional neural network weight optimization method for retinal lesion classification. Background technique [0002] Color fundus images are the most basic examination method for diagnosing ophthalmic diseases. At the same time, fundus images can enable people to detect various eye diseases as early as possible, such as glaucoma, optic neuritis, macular degeneration, etc., which is convenient for timely and effective treatment. Early diagnosis and timely treatment can effectively reduce the prevalence. However, due to the large population in China and the relatively limited number of ophthalmologists, it takes a lot of time to rely solely on doctors to diagnose eye diseases, so other methods are urgently needed for large-scale screening. Computer-aided diagnosis can not only greatly reduce the workload of doctors, but also has the advantages of...

AI Technical Summary

Problems solved by technology

However, due to the complexity of fundus images, the use of traditional convolutional neural networks for multi-label classification of fundus images can easily cause the gradient descent algorithm to fall into a local optimal solution.

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