Moving Healthcare AI-Support Systems for Visually Detectable Diseases onto Constrained Devices

Watt, Tess; Chrysoulas, Christos; Barclay, Peter J

Abstract

Image classification usually requires connectivity and access to the cloud which is often limited in many parts of the world, including hard to reach rural areas. TinyML aims to solve this problem by hosting AI assistants on constrained devices, eliminating connectivity issues by processing data within the device itself, without internet or cloud access. This pilot study explores the use of tinyML to provide healthcare support with low spec devices in low connectivity environments, focusing on diagnosis of skin diseases and the ethical use of AI assistants in a healthcare setting. To investigate this, 10,000 images of skin lesions were used to train a model for classifying visually detectable diseases (VDDs). The model weights were then offloaded to a Raspberry Pi with a webcam attached, to be used for the classification of skin lesions without internet access. It was found that the developed prototype achieved a test accuracy of 78% and a test loss of 1.08.

Citation

Watt, T., Chrysoulas, C., & Barclay, P. J. Moving Healthcare AI-Support Systems for Visually Detectable Diseases onto Constrained Devices

• Working Paper Type: Working Paper
• Deposit Date: Aug 27, 2024
• Publicly Available Date: Sep 2, 2024
• Keywords: tinyML, computation offloading, artificial intelligence, machine learning, computer vision, image classification
• Publisher URL: https://arxiv.org/abs/2408.08215