Oral cancer segmentation and identification system based on histopathological images using MaskMeanShiftCNN and SV-OnionNet

Abstract

Background

Oral squamous cell carcinoma (OSCC) is the most common type of oral cancer and a significant threat to public health because of its high mortality rate. Early detection of OSCC is crucial for successful treatment and improved survival rates, but traditional diagnostic methods, such as biopsy, are time-consuming and require expert analysis. Deep learning algorithms have shown promise in detecting various cancers, including OSCC. However, accurately detecting OSCC on histopathological images remains challenging because of tumor heterogeneity.

Methods

This study proposes two new deep learning approaches, MaskMeanShiftCNN and SV-OnionNet, for segmenting and identifying OSCC. MaskMeanShiftCNN uses color, texture, and shape features to segment OSCC regions from input images, while SV-OnionNet is suitable for identifying OSCC at an early stage from histopathological images.

Results

The proposed approaches outperformed existing methods for OSCC detection, achieving a classification accuracy of 98.94 %, sensitivity of 98.96 %, specificity of 97.18 %, and error rate of 1.05 %. These results demonstrate the effectiveness of the proposed approaches in accurately detecting OSCC and potentially improving the efficiency of OSCC diagnosis.

Conclusion

The proposed deep learning approaches, MaskMeanShiftCNN and SV-OnionNet accurately detected OSCC in input and histopathological images. These approaches can improve the efficiency and accuracy of OSCC diagnosis, ultimately improving patient outcomes.