Performance of image processing analysis and a deep convolutional neural network for the classification of oral cancer in fluorescence visualization.

Abstract

The aim of this prospective study was to determine the effectiveness of screening using image processing analysis and a deep convolutional neural network (DCNN) to classify oral cancers using non-invasive fluorescence visualization. The study included 1076 patients with diseases of the oral mucosa (oral cancer, oral potentially malignant disorders (OPMDs), benign disease) or normal mucosa. For oral cancer, the rate of fluorescence visualization loss (FVL) was 96.9%. Regarding image processing, multivariate analysis identified FVL, the coefficient of variation of the G value (CV), and the G value ratio (VRatio) as factors significantly associated with oral cancer detection. The sensitivity and specificity for detecting oral cancer were 96.9% and 77.3% for FVL, 80.8% and 86.4% for CV, and 84.9% and 87.8% for VRatio, respectively. Regarding the performance of the DCNN for image classification, recall was 0.980 for oral cancer, 0.760 for OPMDs, 0.960 for benign disease, and 0.739 for normal mucosa. Precision was 0.803, 0.821, 0.842, and 0.941, respectively. The F-score was 0.883, 0.789, 0.897, and 0.828, respectively. Sensitivity and specificity for detecting oral cancer were 98.0% and 92.7%, respectively. The accuracy for all lesions was 0.851, average recall was 0.860, average precision was 0.852, and average F-score was 0.849.