Screening of oral potentially malignant disorders and oral cancer using deep learning models

Oral cancer though preventable, shows high mortality and affect the overall quality of life when detected in late stages. Screening techniques that enable early diagnosis are the need of the hour. The present work aims to evaluate the effectiveness of AI screening tools in the diagnosis of OPMDs and Oral cancers via native or web-application (cloud) using smart phone devices. We trained and tested two deep learning models namely DenseNet201 and FixCaps using 518 images of the oral cavity. While DenseNet201 is a pre-trained model, we modified the FixCaps model from capsule network and trained it ground up. Standardized protocols were used to annotate and classify the lesions (suspicious vs. non-suspicious). In terms of model performance, DenseNet201 achieved an F1 score of 87.50% and AUC of 0.97; while FixCaps exhibited F1 score of 82.8% and AUC of 0.93. DenseNet201 model (20 M) serves as a robust screening model (accuracy 88.6%) that can be hosted on a web-application in the cloud servers; while the adapted FixCaps model with its low parameter size of 0.83 M exhibits comparable accuracy (83.8%) allowing easy transitioning into a native phone-based screening application.