Deep Convolutional Neural Networks for Accurate Diagnosis of Nasopharyngeal Carcinoma in pCLE Images

Objectives/Hypothesis: Probe-based confocal laser endomicroscopy(pCLE) can be used for the real-time cell-level optical biopsy of nasopharyngeal lesions in vivo. The development and validation of deep-learning-based automatic diagnosis of pCLE images are essential to the rapid screening and detection of nasopharyngeal carcinoma. Methods: From May 1, 2017, to November 30, 2017, the pCLE images were consecutively collected as samples in data sets from 31 patients with highly suspected NPC. The pCLE images were divided into training, cross-validation, and test sets to train, validate and test the proposed model. The proposed model's accuracy, sensitivity, and specificity are using pathological diagnosis as the gold standard. Analysis of the data is from May 15, 2019, to May 31, 2020. Results: The data set contains 3623 pCLE images in total. Eighty percent of the images were selected randomly as the training set, then another 10% as the validation set, and the last 10% as the test set. We proposed a deep learning model based on ResNet50. On the test data set, the model achieved an overall detection accuracy of 94.7%. The proposed model has higher accuracy, over 20.8%, compared to the best traditional supervised machine learning. Conclusions: Our algorithm automatically detected the nasopharyngeal carcinoma through screening images, which conduces to the otolaryngologists in their diagnosis. This deep-learning-based framework will be of great importance in the rapid screening and detecting of NPC.