Improved Deeplabv3+-based multi-class segmentation model of oral cancer using histopathology images

Early diagnosis of oral cancer is particularly important as it can significantly increase the chances of successfully treating skin cancer and recovering. However, Histopathology image analysis (HIA) traditionally used to perform is time-consuming and requires expert knowledge. Therefore, we propose an oral cancer Multi-class segmentation method called Sc-deeplabv3+. Our model introduces a module called SCConv into the deeplabv3+ model, which uses convolutional neural network (CNN) to compress spatial and channel redundancy between features to highlight important features. We evaluated our model on the ORA-N dataset. The experimental results demonstrate that our proposed model can effectively obtain good performance in the task of oral cancer segmentation. The average delivery of the dice coefficient is 82%, which is 2.7% higher than the baseline deeplabv3+, verifying that our method outperforms existing methods.