Focus: Edge-AI solutions for healthcare and
industry 4.0 (4IR) applications using resource
constrained embedded devices
Dr. Mohammed Zubair has successfully completed ML research
projects across varying disciplines including healthcare, chemicals
industry, energy sector and wireless communications. Currently he
is working with the University of São Paulo (Brazil) and the
University of St. Andrews (U.K.) on developing intelligent edge
vision models for screening Neglected Tropical Diseases. Dr.
Mohammed Zubair holds a Master's degree in Electronics &
Electrical Engineering and a PhD in Electronics Engineering, both
from the University of Dundee, U.K. He is also a Senior Member
of the Institute of Electrical and Electronics Engineers (IEEE) USA.
Diagnosing Oral Cavity Cancer (OCC) in its initial stages is an effective way to reduce patient mortality. However, current pre-screening solutions are manual and the resultant clinical treatment isn’t cost-effective for the average individual, primarily in developing nations. We present an automated and inexpensive pre-screening solution utilizing Artificial Intelligence (AI) deployed on embedded edge devices to detect benign and pre-malignant superficial oral tongue lesions. The proposed machine vision solution utilizes a clinically annotated photographic dataset of 9 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.