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Article Abstract
Diabetes mellitus is a worldwide epidemic that leads to significant changes in foot shape, deformities, and ulcers. Precise classification of diabetic foot not only helps identify foot abnormalities but also facilitates personalized treatment and preventive measures through the engineering design of foot orthoses. In this study, we propose a novel deep learning method based on DiffusionNet which incorporates a self-attention mechanism and external features to classify the foot types of diabetic patients into six categories by using simple 3D foot images directly. Our approach achieves a high accuracy of 82.9% surpassing existing machine and deep learning methods. The proposed model offers a cost-effective way to analyse foot shapes and facilitate the customization process for both the footwear industry and medical applications.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC12012058 | PMC |
| http://dx.doi.org/10.1038/s41598-025-98471-5 | DOI Listing |
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