Hardware Deployable Edge-AI Solution for Prescreening of Oral Tongue Lesions Using TinyML on Embedded Devices

Abstract

Diagnosing oral cavity cancer (OCC) in its initial stages is an effective way to reduce patient mortality. However, current prescreening solutions are manual and the resultant clinical treatment is not cost effective for the average individual, primarily in developing nations. In this letter, we present an automated and inexpensive prescreening solution utilizing artificial intelligence (AI) deployed on embedded edge devices to detect benign and premalignant superficial oral tongue lesions. The proposed machine vision solution utilizes a clinically annotated photographic dataset of nine types of superficial oral tongue lesions to retrain a MobileNetV2 neural network using transfer learning. In this approach, we also utilized TensorFlow Lite for Microcontrollers to quantize a 32-bit floating point (float32) precision model into an 8-bit integer (int8) model for deployment on power and resource-constrained OpenMV Cam H7 Plus embedded edge device. The quantized int8 model was able to detect the nine tongue lesions with an accuracy of 98.69% on the test set. More than 60% reduction in on-device RAM and flash memory usage were measured for the int8 model when compared to the equivalently performing float32 model for relatively the same inference speed (~1.1 ms) on the target edge device.