Work

Moving Healthcare AI Support Systems for Visually Detectable Diseases to Constrained Devices

Year: 2024

Type: article

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. Tiny machine learning (tinyML) aims to solve this problem by hosting artificial intelligence (AI) assistants on constrained devices, eliminating connectivity issues by processing data within the device itself, without Internet or cloud access. This study explores the use of tinyML to provide healthcare support with low-spec devices in low-connectivity environments, focusing on the diagnosis of skin diseases and the ethical use of AI assistants in a healthcare setting. To investigate this, 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% when trained on the HAM10000 dataset, and a test accuracy of 85% when trained on the ISIC 2020 Challenge dataset.

Source: Applied Sciences

DOI: 10.3390/app142411474

Authors: Tess Watt, Christos Chrysoulas, Peter J. Barclay, Brahim El Boudani, Grigorios Kalliatakis

Institutions: Heriot-Watt University, Edinburgh Napier University, London South Bank University, FORTH Institute of Computer Science, Foundation for Research and Technology Hellas

Language: English

Topic: Cutaneous Melanoma Detection and Management

Subfield: Oncology

Field: Medicine

Domain: Health Sciences

SDGs: Good health and well-being, Industry, innovation and infrastructure, Reduced inequalities

Open Access status: gold

Estimated APC paid: $2,728