Energy-Efficient AI for Medical Diagnostics: Performance and Sustainability Analysis of ResNet and MobileNet
Year: 2025
Type: book-chapter
Abstract: Artificial intelligence (AI) has transformed medical diagnostics by enhancing the accuracy of disease detection, particularly through deep learning models to analyze medical imaging data. However, the energy demands of training these models, such as ResNet and MobileNet, are substantial and often overlooked; however, researchers mainly focus on improving model accuracy. This study compares the energy use of these two models for classifying thoracic diseases using the well-known CheXpert dataset. We calculate power and energy consumption during training using the EnergyEfficientAI library. Results demonstrate that MobileNet outperforms ResNet by consuming less power and completing training faster, resulting in lower overall energy costs. This study highlights the importance of prioritizing energy efficiency in AI model development, promoting sustainable, eco-friendly approaches to advance medical diagnosis.
DOI: 10.3233/shti250585
Authors: Zia Ur Rehman, Uzair Hassan, Saif Ul Islam, Parisis Gallos, Jalil Boudjadar
Language: English
Topic: Green IT and Sustainability
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4410447262