Convolutional neural network misclassification analysis in oral lesions: an error evaluation criterion by image characteristics.

ABSTRACT

OBJECTIVE: This retrospective study analyzed the errors generated by a convolutional neural network (CNN) when performing automated classification of oral lesions according to their clinical characteristics, seeking to identify patterns in systemic errors in the intermediate layers of the CNN. STUDY DESIGN: A cross-sectional analysis nested in a previous trial in which automated classification by a CNN model of elementary lesions from clinical images of oral lesions was performed. The resulting CNN classification errors formed the dataset for this study. A total of 116 real outputs were identified that diverged from the estimated outputs, representing 7.6% of the total images analyzed by the CNN. RESULTS: The discrepancies between the real and estimated outputs were associated with problems relating to image sharpness, resolution, and focus; human errors; and the impact of data augmentation. CONCLUSIONS: From qualitative analysis of errors in the process of automated classification of clinical images, it was possible to confirm the impact of image quality, as well as identify the strong impact of the data augmentation process. Knowledge of the factors that models evaluate to make decisions can increase confidence in the high classification potential of CNNs.