A Novel Dataset And Efficient Deep Learning Framework For Automated Grading Of Renal Cell Carcinoma From Kidney Histopathology Images

This study proposed a robust and computationally efficient fully automated Renal Cell Carcinoma Grading Network (RCCGNet) from kidney histopathology images. The proposed RCCGNet contains a shared channel residual (SCR) block which allows the network to learn feature maps associated with different versions of the input with two parallel paths. Experiments were conducted on two datasets, the proposed dataset with five different grades and the BreakHis dataset for eight class-classification. Results showed that the proposed RCCGNet is superior in comparison with the eight most recent classification methods in terms of prediction accuracy and computational complexity.