Work

Automated Hallux Valgus Detection from Foot Photos Using CBAM-Enhanced MobileNetV3 with Data Augmentation

Year: 2025

Type: article

Abstract: Hallux valgus is a common foot deformity. Traditional diagnosis mainly relies on X-ray images, which present radiation risks and require professional equipment, limiting their use in daily screening. In addition, in large-scale community screenings and resource-limited regions, where rapid processing of numerous patients is required, access to radiographic equipment or specialists may be constrained. Therefore, this study improves the MobileNetV3 model to automatically determine the presence of hallux valgus from digital foot photographs. In this study, we used 2934 foot photos from different organizations, combined with the segment anything model (SAM) to extract foot regions and replace the photo backgrounds to simulate different shooting scenarios, and used data enhancement techniques such as rotations and noise to extend the training set to more than 10,000 images to improve the diversity of the data and the model’s generalization ability. We evaluated several classification models and achieved over 95% accuracy, precision, recall, and F1 score by training the improved MobileNetV3. Our model offers a cost-effective, radiation-free solution to reduce clinical workload and enhance early diagnosis rates in underserved areas.

Source: Electronics

DOI: 10.3390/electronics14112258

Authors: Xuhui Fang, Pengfei Li, Di Wu, Yushan Pan, Hao Wang

Institutions: Xidian University, Beijing Tongren Hospital, Norwegian University of Science and Technology, Xi’an Jiaotong-Liverpool University

Language: English

Topic: Advanced Neural Network Applications

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $2,896