Evaluation of artificial neural networks for the detection of esophagus tumor cells in microscopic hyperspectral images

Abstract

Microscopic analysis of histological slides of cancer tissue samples is standardly performed under white light microscopy. Researchers demonstrated the potential of artificial intelligence (AI) methods for the automatic identification of tumor cells. Hyperspectral imaging (HSI) combined with AI approaches can improve the accuracy, reliability, and time of the analysis. In this work, a HSI camera was coupled with a standard microscope to acquire microscopic hyperspectral (HS) images of stained histological slides of esophagus cancer tissue of 95 patients. The HS images were analyzed with deep learning algorithms to discriminate healthy cells (squamous epithelium) and tumors (stroma tumor and esophagus adenocarcinoma EAC). Five models were considered: a 2D CNN, a 2D CNN preserving the spatial relationship between spectral layers, a 3D CNN, a pre-trained 3D CNN and a recurrent neural network (RNN). They were evaluated using a leave-one-patient-out cross-validation. The predicted two classes were visualized with false colors. The RNN obtained the highest quantitative results with an accuracy of 0.791, an AUC of 0.79 and a computing time of 7.57 s per 10,000 patches. The best visual result was obtained on two selected HS images with the 2D CNN model. The performance of the automatic classification was higher on tissue which has not been treated with previous neoadjuvant therapy. The combination of HSI with deep learning method is promising for the automatic analysis of histological slides for cancer diagnosis.