Abstract: Hepatic echinococcosis is a serious regional parasitic disease. The diagnosis and classification of lesions mainly rely on the subjective judgment of the clinician on ultrasound images. In areas with weak medical conditions， the screening and diagnosis of the disease can easily be misjudged. In order to improve the diagnosis efficiency and accuracy of liver hydatid disease， this paper combines deep learning algorithms to apply the deep DenseNet network to the image classification problem of liver hydatid disease， and uses the powerful feature extraction capabilities of deep convolutional neural networks to construct liver hydatid classification model. In addition， in order to be able to provide more reliable image input information， the ROI-based preprocessing method is used to extract the lesion ROI area of the original image. Finally， the model is evaluated and verified on the constructed data set， and the accuracy can reach 93%， and by using gradient weighted class activation map for visual analysis， it showes that the model has strong robustness and better classification effect.

Key words: hepatic echinococcosis, ultrasonic, deep learning, computer aided diagnosis, image classification