%0 Journal Article %A AN Fengping %A LI Xiaowei %A CAO Xiang %T Medical Image Classification Algorithm Based on Weight Initialization-Sliding Window CNN %D 2022 %R 10.3778/j.issn.1673-9418.2011091 %J Journal of Frontiers of Computer Science & Technology %P 1885-1897 %V 16 %N 8 %X

Deep learning has the following problems in medical image classification application: first, it is impossible to construct a deep learning model hierarchy for medical image properties; second, the network initialization weight of the deep learning model has not been optimized. To this end, this paper starts from the perspective of network optimization, and then improves the nonlinear modeling ability of the network through optimization methods. Then, this paper proposes a new network weight initialization method, which alleviates the problem that the initialization theory of existing deep learning is limited by the nonlinear unit type, and increases the potential of neural network to deal with different visual tasks. At the same time, in order to make full use of the characteristics of medical images, this paper deeply studies the multi-column convolutional neural network framework and finds that through changing the number of features and the convolution kernel size of different levels of convolutional neural networks, it can construct different convolutional neural network models to better adapt to the medical characteristics of the medical images to be processed and train the obtained heterogeneous multi-column convolutional neural networks. Finally, the classification task of medical images is completed by the method proposed in this paper. Based on the above ideas, this paper proposes a medical classification algorithm based on weight initialization-sliding window fusion of multi-layer convolutional neural networks. The methods of this paper are used to classify breast mass classification, brain tumor tissue classification experiment and medical image database classification. The experimental results show that the proposed method not only has higher average accuracy than traditional machine learning and other deep learning methods, but also has better stability and robustness.

%U http://fcst.ceaj.org/EN/10.3778/j.issn.1673-9418.2011091