Resnet-V: a residual networks classification algorithm of keratoconus

Le résumé fourni par la source

In this paper, in view of the problems of large computation and complex network model in the current corneal disease classification algorithm, a new residual network model Resnet-V is designed on the basis of feature extraction ResNet-50. The algorithm extracts and reduces the feature of images through the residual network, and maps the image feature vectors to multiple hash tables through E2LSH to achieve efficient image retrieval and classification. The residual network is used to extract and reduce the feature data to obtain a low-dimensional feature vector representation. The use of a pooling layer and downsampling in the residual network will reduce the spatial resolution of the feature map, so that many details are lost, thereby affecting the accuracy of the model for graph classification, based on the feature vector of the residual network, select the appropriate hash function and parameters to realize the hash encoding of the feature vector. Experiments were conducted on iChallenge-PM, a publicly available medical dataset. Experimental results show that the proposed algorithm achieves high accuracy and low loss value, and the classification recognition rate is better than that of other model algorithms.

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