Using hyperspectral imaging automatic classification of gastric cancer grading with shallow residual network

The gastric cancer grading of a patient determines their clinical treatment plan. We use hyperspectral imaging (HSI) gastric cancer section data to automatically classify the three different cancer grades (low grade, intermediate grade, and high grade) and healthy tissue. This paper proposed the use of HSI data combined with a shallow residual network (SR-Net) as the classifier. We collected hyperspectral data from gastric section of 30 participants, with the wavelength range of hyperspectral data is 374 nm to 990 nm. We compared the classification results between hyperspectral data and color images. The results show that using hyperspectral data achieved an average classification accuracy of 91.74% by SR-Net, 12.5% higher than the color image. In addition, we applied a modified SR-Net incorporated direct down-sampling, asymmetric filters, and global average pooling to reduce the parameters and floating-point operations. Compared with the regular residual network with the same number of blocks, the floating-point operations of SR-Net are one order of magnitude less. The experimental results show that hyperspectral data with SR-Net can achieve cutting-edge performance with minimum computational cost, and therefore has potential in the study of gastric cancer grading.