Pathological Images Classification based on the Pretrained Convolutional Neural Network

Abstract

Convolutional neural network is more and more used to solve the problem of images classification, but the scale of the dataset in this filed is too small compared with that of natural image recognition, which leads to the slow convergence speed of the neural network, and the instability of the model, which is difficult to be used to predict the results. In this paper, the residual network is applied to the PCam dataset to realize the recognition of tumour cells, and pre-trained through the ImageNet dataset. In the same training epochs, the performance of the pre-train network is much higher than that of the randomly initialized network, which significantly improves the convergence speed of the neural network. In addition, the performance of the pre-train network can converge faster when the amount of data is sufficient, and in the case oof medical image recognition with less data, the performance of the pre-train network is much higher than that of the random initialization network.