Photo Recognition of Alzheimer’s disease Using Convolutional Neural Network through Artificial Intelligence

Abstract

As an algorithm with excellent performance, convolutional neural network has been widely used in the field of image processing and achieved good results by relying on its own local receptive fields, weight sharing, pooling, and sparse connections. In order to improve the convergence speed and recognition accu- racy of the convolutional neural network algorithm, this paper proposes a new convolutional neural network algorithm. First, a recurrent neural network is introduced into the convolutional neural network, and the deep features of the image are learned in parallelusingtheconvolutionalneuralnetworkandtherecurrent neural network. Secondly, according to the idea of ResNet’sskip convolution layer, a new residual module ShortCut3-ResNet is constructed. Then, a dual optimization model is established to realize the integrated optimization of the convolution and full connection process. This paper helps a person to recognisethe severity of a person with Alzheimer ‘s disease by simply viewing the image of the affected area. Alzheimer’s disease can be classified as early-onset or late-onset. The signs and symptoms of the early-onset form appear between a person’s thirties and mid-sixties, while the late-onset form appears during or after a person’smid-sixties