Segmentation and classification of renal tumors based on convolutional neural network

Kidney tumors are the second most frequent urology tumors. They are of many types, mostly existing as malignant tumors. In order to improve the accuracy of segmentation and classification of kidney tumors, this paper proposes to build a model of simultaneous segmentation and classification of kidney tumors based on convolutional neural networks to assist medical experts in diagnosis. A two-task neural network 2D SCNet is proposed by combining kidney tumor segmentation and classification. Based on our proposed framework, classification can feed back the global contextual information of the network, and segmentation can make the network focus on local features and regions of interest (ROI). Both tasks jointly promote network feature learning and both increase each other prior information. The combination of segmentation and classification of 2D SCNet can achieve an accuracy rate of 99.5% in both benign and malignant classification. The results of the 2D SCNet + three-label segmentation reached Dice coefficients of 0.946 and 0.846, respectively. Compared with PSPNet, our network kidney and tumor segmentation results are improved by 4.9% and 5.0%, respectively, which shows that the addition of classification module is beneficial to the learning of segmentation network. From the cross-validation results, we can see that 2D SCNet and the two-step segmentation strategy can obtain better results in the segmentation and classification tasks of kidney tumors. The base network of 2D SCNet can extract networks for any feature. This paper compares Res Net 50+ PPM and Dense Net as the results of segmentation and classification of the base network. Res Net 50+ PPM obtains better results. 2D SCNet can help to segment and examine kidney tumors more efficiently and accurately