Classification of Metabolic Dysfunction-Associated Steatotic Liver Disease in Thermal Images Using Time Series
Abstract
Metabolic dysfunction-associated steatotic liver disease affects approximately 30% of the global population. Its clinical course may progress to severe conditions such as cirrhosis and hepatocellular carcinoma. In this context, thermography has emerged as a non-invasive, yet challenging, method for screening for the disease. This study compared temporal, spatial, and spatio-temporal integration approaches for liver disease classification using thermal image time series. The experiments achieved a precision of 66% and sensitivity of 73%, demonstrating that preserving temporal evolution along with spatial patterns results in better performance.
References
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Tapper, E. B. and Loomba, R. (2018). Noninvasive imaging biomarker assessment of liver fibrosis by elastography in nafld. Nature Reviews Gastroenterology & Hepatology, 15(5):274–282.
Tu, Z. et al. (2022). Maxvit: Multi-axis vision transformer. In European Conference on Computer Vision, pages 459–479. Springer Nature Switzerland.
Vaswani, A. et al. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 30.
Yadav, S. S. and Jadhav, S. M. (2019). Deep convolutional neural network based medical image classification for disease diagnosis. Journal of Big Data, 6(1):1–18.
Zuluaga-Gomez, J., Masry, Z. A., Benaggoune, K., et al. (2021). A cnn-based methodology for breast cancer diagnosis using thermal images. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 9(2):131–145.
Bedossa, P. (2016). Histological assessment of nafld. Digestive Diseases and Sciences, 61(5):1348–1355.
Brioschi, M. L., Lin, T. Y., Colman, D., Silva, F. M. R. M., and Teixeira, M. J. (2006). Imaginologia infravermelha no estudo avançado da dor de origem visceral. Rev Dor: Pesquisa, Clínica e Terapêutica, 7(4):825–826.
Cotrim, H. P., Parise, E. R., Figueiredo-Mendes, C., et al. (2016). Nonalcoholic fatty liver disease brazilian society of hepatology consensus. Arquivos de Gastroenterologia, 53:118–122.
Dosovitskiy, A. (2020). An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929.
EASL, EASD, and EASO (2024). Clinical practice guidelines on the management of metabolic dysfunction-associated steatotic liver disease (masld). Journal of Hepatology, 81(3):492–542.
Elshawi, R., Maher, M., and Sakr, S. (2019). Automated machine learning: State-of-the-art and open challenges. arXiv preprint arXiv:1906.02287.
Farias, M. V. S., Silva, I. F. S., Silva, A. C., et al. (2025). Method for detecting non-alcoholic fatty liver disease in abdominal thermography time series using temporal convolutional networks. Elsevier (To be published).
Farooq, M. A. and Corcoran, P. (2020). Infrared imaging for human thermography and breast tumor classification using thermal images. In 2020 31st Irish Signals and Systems Conference (ISSC), pages 1–6. IEEE.
Friedman, S. L., Neuschwander-Tetri, B. A., Rinella, M., and Sanyal, A. J. (2018). Mechanisms of nafld development and therapeutic strategies. Nature Medicine, 24(7):908–922.
Khan, S. et al. (2022). Transformers in vision: A survey. ACM Computing Surveys (CSUR), 54(10s):1–41.
Kohavi, R. (1995). A study of cross-validation and bootstrap for accuracy estimation and model selection. In International Joint Conference on Artificial Intelligence (IJCAI), volume 14, pages 1137–1145.
Lahiri, B. B., Subramaniam, B., Jayakumar, T., and Philip, J. (2012). Medical applications of infrared thermography: A review. Infrared Physics & Technology, 55:221–235.
Lea, C., Flynn, M. D., Vidal, R., Reiter, A., and Hager, G. D. (2017). Temporal convolutional networks for action segmentation and detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 156–165.
Li, Z., Liu, F., Yang, W., Peng, S., and Zhou, J. (2021). A survey of convolutional neural networks: analysis, applications, and prospects. IEEE Transactions on Neural Networks and Learning Systems.
Lin, T. Y., Goyal, P., Girshick, R., He, K., and Dollár, P. (2017). Focal loss for dense object detection. In Proceedings of the IEEE International Conference on Computer Vision, pages 2980–2988.
Liu, Z. et al. (2021). Swin transformer: Hierarchical vision transformer using shifted windows. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 10012–10022.
Liu, Z. et al. (2022a). A convnet for the 2020s. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 11976–11986.
Liu, Z. et al. (2022b). Video swin transformer. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 3202–3211.
Olaru, A. (2017). Infrared thermographic evaluation of patients with metastatic vertebral fractures after combined minimal invasive surgical treatment. The Moldovan Medical Journal, 60:22–25.
Ozougwu, J. C. (2017). Physiology of the liver. International Journal of Research in Pharmacy and Biosciences, 4(8):13–24.
Pinto, D. M. et al. (2021). Classificação de esteatose hepática não alcoólica em imagens térmicas da região do fígado utilizando redes neurais convolucionais. In Simpósio Brasileiro de Computação Aplicada à Saúde (SBCAS), pages 302–312. SBC.
Powell, E. E., Wong, V. W. S., and Rinella, M. (2021). Non-alcoholic fatty liver disease. The Lancet, 397(10290):2212–2224.
Rinella, M. E., Lazarus, J. V., Ratziu, V., Francque, S. M., et al. (2023). A multisociety delphi consensus statement on new fatty liver disease nomenclature. Hepatology, 78(6):1966–1986.
Santana, J. T., Mota, A. V. H., Gonzaga, Y. H. G., et al. (2021). Perfil metabólico e antropométrico dos pacientes obesos e não obesos portadores de esteatose hepática não alcoólica. Revista Eletrônica Acervo Saúde, 13(2):e5525.
Silva, M. P., Silva, A. C., and de Paiva, A. C. (2024). Classification of non-alcoholic fatty liver disease in thermal images of the liver using a siamese neural network. In Brazilian Conference on Intelligent Systems, pages 260–269. Springer Nature Switzerland.
Tapper, E. B. and Loomba, R. (2018). Noninvasive imaging biomarker assessment of liver fibrosis by elastography in nafld. Nature Reviews Gastroenterology & Hepatology, 15(5):274–282.
Tu, Z. et al. (2022). Maxvit: Multi-axis vision transformer. In European Conference on Computer Vision, pages 459–479. Springer Nature Switzerland.
Vaswani, A. et al. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 30.
Yadav, S. S. and Jadhav, S. M. (2019). Deep convolutional neural network based medical image classification for disease diagnosis. Journal of Big Data, 6(1):1–18.
Zuluaga-Gomez, J., Masry, Z. A., Benaggoune, K., et al. (2021). A cnn-based methodology for breast cancer diagnosis using thermal images. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 9(2):131–145.
Published
2026-06-01
How to Cite
OLIVEIRA, Wenderson Arthur Dutra; BRITO, Francisco Roberto Cantanhede; PINTO, Daniel Moreira; SILVA, Aristófanes Corrêa; MARTINS FILHO, Henrique Manoel de Araujo.
Classification of Metabolic Dysfunction-Associated Steatotic Liver Disease in Thermal Images Using Time Series. In: BRAZILIAN SYMPOSIUM ON COMPUTING APPLIED TO HEALTH (SBCAS), 26. , 2026, Ouro Preto/MG.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2026
.
p. 265-276.
ISSN 2763-8952.
DOI: https://doi.org/10.5753/sbcas.2026.20720.
