Oral Cancer Detection using Mobile Vision Technology

Lena D. Swamikannan, Akshay Bhagwan Sonawane, C.S. Mani, Lakshmi Narayana, Lakshman Tamil, Jay S. Patel

Keywords: oral cancer, deep neural networks, mobile vision technology

Abstract: Oral cancer is the 13th most common cancer, affecting 380,000 people globally. The biggest challenge is that in its initial stage, cancer can go unnoticed until it reaches the most advanced, difficult-to-treat stages. Although a 90% survival rate is assured when diagnosed earlier, early-stage detection requires expensive periodic dental check-ups. Under these circumstances, converting a smartphone into a cancer screening tool has the potential to reduce cancer incidences. Mobile vision technology is a promising platform for early diagnosis of oral cancer. The aim of this research is to launch a telemedical mobile application for Oral Cancer Detection (OCD) using deep learning as a backend. This paper details experiments with various lightweight machine learning architectures, including MobileNetV3Large, which achieved an 84% accuracy, 86% sensitivity and 80% specificity on the test data. By incorporating the machine learning model into the smartphone app, users can capture or upload images for instant or offline screening, ensuring on-device processing that maintains privacy. This application promises to revolutionize oral healthcare accessibility and delivery.

Track: 10. Digital health

Registration Id: 45N34DWJNNB

Submission Number: 252