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Image classification of hepatic echinococcosis based on convolutional neural network

To explore the application of convolutional neural network(CNN) in CT image diagnosis of hepatic hydatid disease, we implement a classification using deep learning. An improved Lenet-5 was proposed to conduct image classification experiments and achieved 97.84 precision. Dropout layer and regularization strategy are adopted to prevent overfitting, which improves the performance of experimental classification. Collected CT images of normal liver, single cystic liver hydatidosis, polycystic hepatic echinococcosis and hepatic cyst were processed by normalization, median filtering denoising and data enhancement. Finally, the deconvolution is used to realize the visualization of feature map for assisting doctors to explore the potential features of diseases. The results show that the improved convolutional neural network has achieved good results in the classification task, and it is expected to provide auxiliary diagnosis of liver hydatidosis in practice.