SpineSiamSwin: An IoMT-Driven Siamese Swin Transformer Model Based on Transfer Learning for Intelligent Diagnosis of Spinal Diseases
Document Type
Article
Publication Date
12-26-2025
Identifier/URL
43182515 (Pure)
Abstract
The incidence of spinal diseases has been steadily increasing in recent years, with affected populations becoming progressively younger. With the rapid development of artificial intelligence and medical image processing technologies, automated intelligent diagnostic algorithms based on spinal X-ray images have been continuously developed. However, current deep learning–based diagnostic methods for spinal diseases face limitations such as the limited availability of labeled data and subtle inter-class feature differences. To address these challenges in spinal X-ray image diagnosis, this study proposes a novel Siamese Swin Transformer model based on transfer learning, within the Internet of Medical Things (IoMT) framework—referred to as the SpineSiamSwin model. The model adopts a Siamese network structure and leverages metric learning loss functions to maximize the distance between different classes in the embedding space, thereby enhancing the model’s sensitivity to subtle structural differences between disease categories. Extensive experiments on real-world spinal X-ray datasets demonstrate the effectiveness and practical applicability of the SpineSiamSwin model.
Repository Citation
Lan, C.,
Lin, J.,
Wang, K.,
Wang, Y.,
Wu, Z.,
Wang, Y.,
& Wu, H.
(2025). SpineSiamSwin: An IoMT-Driven Siamese Swin Transformer Model Based on Transfer Learning for Intelligent Diagnosis of Spinal Diseases. IEEE Internet of Things Journal.
https://corescholar.libraries.wright.edu/ee/147
DOI
10.1109/JIOT.2025.3648458
