A one-dimensional convolutional neural network based deep learning for high accuracy classification of transformation stages in esophageal squamous cell carcinoma tissue using micro-FTIR.

Among the most frequently diagnosed cancers in developing countries, esophageal squamous cell carcinoma (ESCC) ranks among the top six causes of death. It would be beneficial if a rapid, accurate, and automatic ESCC diagnostic method could be developed to reduce the workload of pathologists and improve the effectiveness of cancer treatments. Using micro-FTIR spectroscopy, this study classified the transformation stages of ESCC tissues. Based on 6,352 raw micro-FTIR spectra, a one-dimensional convolutional neural network (1D-CNN) model was constructed to classify-five stages. Based on the established model, more than 93% accuracy was achieved at each stage, and the accuracy of identifying proliferation, low grade neoplasia, and ESCC cancer groups was achieved 99% for the test dataset. In this proof-of-concept study, the developed method can be applied to other diseases in order to promote the use of FTIR spectroscopy in cancer pathology.