Green AI e Data Augmentation para Triagem de Úlceras do Pé Diabético no Brasil: um Pipeline Eficiente, Sustentável e Socialmente Orientado
Resumo
As úlceras do pé diabético são uma complicação grave do diabetes mellitus que sobrecarrega o Sistema Único de Saúde [Armstrong et al. 2017, DATASUS 2025]. Propomos um pipeline de visão computacional orientado à saúde pública para classificar e segmentar essas lesões, guiado por eficiência computacional (Green AI) e robustez com dados limitados. Ele combina EfficientNet-B0 e MobileNetV3-Large com adaptação Batch Normalizationonly, MixUp/CutMix, FixRes e inferência adaptativa por entropia, auditando energia e CO2 com o CodeCarbon [Schmidt et al. 2021] nos desafios DFUC2020/DFUC2021. Os modelos BN-only atingem AUC, F1-macro e AUPRC competitivos com custo energético muito inferior ao full fine-tune.
Palavras-chave:
Green AI, Data Augmentation, Úlceras do Pé Diabético, Saúde Pública, Visão Computacional, Eficiência Computacional, Sustentabilidade
Referências
Alhasson, H. F. and Alharbi, S. S. (2025). Advances in image-based diagnosis of diabetic foot ulcers using deep learning and machine learning: a systematic review. Biomedicines, 13(12):2928.
Alkhalefah, S., Alturaiki, I., and Altwaijry, N. (2025). Advancing diabetic foot ulcer care: Ai and generative ai approaches for classification, prediction, segmentation, and detection. Healthcare, 13(6):648.
Armstrong, D. G., Boulton, A. J. M., and Bus, S. A. (2017). Diabetic foot ulcers and their recurrence. New England Journal of Medicine, 376(24):2367–2375.
Brasil (2018). Lei nº 13.709, de 14 de agosto de 2018. lei geral de proteção de dados pessoais (lgpd). Diário Oficial da União, Brasília, DF, 15 ago. 2018. Disponível em: [link]. Acesso em: 10 mai. 2026.
Cassidy, B., Kendrick, C., Reeves, N. D., et al. (2022). Diabetic foot ulcer grand challenge 2021: evaluation and summary. In Diabetic Foot Ulcers Grand Challenge (DFUC 2021), Lecture Notes in Computer Science, volume 13183. Springer, Cham.
DATASUS (2025). Morbidade hospitalar do sus (sih/sus): procedimentos relacionados a amputações de membros inferiores. Departamento de Informática do SUS, Ministério da Saúde. Disponível em: [link]. Acesso em: jan. 2025.
Frankle, J., Schwab, D. J., and Morcos, A. S. (2021). Training BatchNorm and only BatchNorm. In International Conference on Learning Representations (ICLR). [link].
Galdran, A., Carneiro, G., and Ballester, M. A. G. (2022). Convolutional nets versus vision transformers for diabetic foot ulcer classification. In Diabetic Foot Ulcers Grand Challenge (DFUC 2021), Lecture Notes in Computer Science, volume 13183. Springer.
Guo, C., Pleiss, G., Sun, Y., and Weinberger, K. Q. (2017). On calibration of modern neural networks. In Proceedings of the 34th International Conference on Machine Learning (ICML), PMLR 70, pages 1321–1330. [link].
Howard, A., Sandler, M., Chu, G., et al. (2019). Searching for MobileNetV3. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pages 1314–1324.
IBGE (2020). Pesquisa nacional de saúde 2019: informações sobre domicílios, acesso e utilização dos serviços de saúde. Technical report, Instituto Brasileiro de Geografia e Estatística, Rio de Janeiro. [link].
IBGE (2022). Censo demográfico 2022: resultados do universo — características da população. Technical report, Instituto Brasileiro de Geografia e Estatística, Rio de Janeiro. Disponível em: [link]. Acesso em: 10 mai. 2026.
IDF (2019). Idf diabetes atlas. Technical report, International Diabetes Federation. [link].
Mehta, S. and Rastegari, M. (2022). Mobilevit: Light-weight, general-purpose, and mobile-friendly vision transformer. In International Conference on Learning Representations (ICLR).
Ministério da Saúde (2020). Vigitel brasil 2019: vigilância de fatores de risco e proteção para doenças crônicas por inquérito telefônico. Technical report, Ministério da Saúde, Brasília. [link].
Niri, R., Zahia, S., Stefanelli, A., et al. (2025). Wound segmentation with u-net using a dual attention mechanism and transfer learning. Journal of Imaging Informatics in Medicine.
Oliveira, T. C. D., Oliveira, A. F., Araújo, L. C., et al. (2025). Digital health technologies for diabetic foot ulcers: a systematic review of clinical evidence, access inequities, and public health integration. International Journal of Environmental Research and Public Health, 22(9):1430.
Schmidt, V., Goyal, K., Joshi, A., et al. (2021). CodeCarbon: estimate and track carbon emissions from machine learning computing.
Schwartz, R., Dodge, J., Smith, N. A., and Etzioni, O. (2020). Green AI. Communications of the ACM, 63(12):54–63.
Strubell, E., Ganesh, A., and McCallum, A. (2019). Energy and policy considerations for deep learning in NLP. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (ACL), pages 3645–3650.
Tan, M. and Le, Q. V. (2019). EfficientNet: rethinking model scaling for convolutional neural networks. In Proceedings of the 36th International Conference on Machine Learning (ICML), PMLR 97, pages 6105–6114. [link].
Touvron, H., Vedaldi, A., Douze, M., et al. (2019). Fixing the train-test resolution discrepancy. In Advances in Neural Information Processing Systems (NeurIPS), volume 32, pages 8250–8260. [link].
Yang, L., Mehta, N., Chen, D., et al. (2020). Resolution adaptive networks for efficient inference. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 2369–2378.
Yap, M. H., Cassidy, B., Pappachan, J. M., et al. (2021). Analysis towards classification of infection and ischaemia of diabetic foot ulcers. In 2021 IEEE EMBS International Conference on Biomedical and Health Informatics (BHI), pages 1–4.
Yun, S., Han, D., Oh, S. J., et al. (2019). CutMix: regularization strategy to train strong classifiers with localizable features. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pages 6023–6032.
Zhang, H., Cisse, M., Dauphin, Y. N., and Lopez-Paz, D. (2018). mixup: beyond empirical risk minimization. In International Conference on Learning Representations (ICLR). [link].
Alkhalefah, S., Alturaiki, I., and Altwaijry, N. (2025). Advancing diabetic foot ulcer care: Ai and generative ai approaches for classification, prediction, segmentation, and detection. Healthcare, 13(6):648.
Armstrong, D. G., Boulton, A. J. M., and Bus, S. A. (2017). Diabetic foot ulcers and their recurrence. New England Journal of Medicine, 376(24):2367–2375.
Brasil (2018). Lei nº 13.709, de 14 de agosto de 2018. lei geral de proteção de dados pessoais (lgpd). Diário Oficial da União, Brasília, DF, 15 ago. 2018. Disponível em: [link]. Acesso em: 10 mai. 2026.
Cassidy, B., Kendrick, C., Reeves, N. D., et al. (2022). Diabetic foot ulcer grand challenge 2021: evaluation and summary. In Diabetic Foot Ulcers Grand Challenge (DFUC 2021), Lecture Notes in Computer Science, volume 13183. Springer, Cham.
DATASUS (2025). Morbidade hospitalar do sus (sih/sus): procedimentos relacionados a amputações de membros inferiores. Departamento de Informática do SUS, Ministério da Saúde. Disponível em: [link]. Acesso em: jan. 2025.
Frankle, J., Schwab, D. J., and Morcos, A. S. (2021). Training BatchNorm and only BatchNorm. In International Conference on Learning Representations (ICLR). [link].
Galdran, A., Carneiro, G., and Ballester, M. A. G. (2022). Convolutional nets versus vision transformers for diabetic foot ulcer classification. In Diabetic Foot Ulcers Grand Challenge (DFUC 2021), Lecture Notes in Computer Science, volume 13183. Springer.
Guo, C., Pleiss, G., Sun, Y., and Weinberger, K. Q. (2017). On calibration of modern neural networks. In Proceedings of the 34th International Conference on Machine Learning (ICML), PMLR 70, pages 1321–1330. [link].
Howard, A., Sandler, M., Chu, G., et al. (2019). Searching for MobileNetV3. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pages 1314–1324.
IBGE (2020). Pesquisa nacional de saúde 2019: informações sobre domicílios, acesso e utilização dos serviços de saúde. Technical report, Instituto Brasileiro de Geografia e Estatística, Rio de Janeiro. [link].
IBGE (2022). Censo demográfico 2022: resultados do universo — características da população. Technical report, Instituto Brasileiro de Geografia e Estatística, Rio de Janeiro. Disponível em: [link]. Acesso em: 10 mai. 2026.
IDF (2019). Idf diabetes atlas. Technical report, International Diabetes Federation. [link].
Mehta, S. and Rastegari, M. (2022). Mobilevit: Light-weight, general-purpose, and mobile-friendly vision transformer. In International Conference on Learning Representations (ICLR).
Ministério da Saúde (2020). Vigitel brasil 2019: vigilância de fatores de risco e proteção para doenças crônicas por inquérito telefônico. Technical report, Ministério da Saúde, Brasília. [link].
Niri, R., Zahia, S., Stefanelli, A., et al. (2025). Wound segmentation with u-net using a dual attention mechanism and transfer learning. Journal of Imaging Informatics in Medicine.
Oliveira, T. C. D., Oliveira, A. F., Araújo, L. C., et al. (2025). Digital health technologies for diabetic foot ulcers: a systematic review of clinical evidence, access inequities, and public health integration. International Journal of Environmental Research and Public Health, 22(9):1430.
Schmidt, V., Goyal, K., Joshi, A., et al. (2021). CodeCarbon: estimate and track carbon emissions from machine learning computing.
Schwartz, R., Dodge, J., Smith, N. A., and Etzioni, O. (2020). Green AI. Communications of the ACM, 63(12):54–63.
Strubell, E., Ganesh, A., and McCallum, A. (2019). Energy and policy considerations for deep learning in NLP. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics (ACL), pages 3645–3650.
Tan, M. and Le, Q. V. (2019). EfficientNet: rethinking model scaling for convolutional neural networks. In Proceedings of the 36th International Conference on Machine Learning (ICML), PMLR 97, pages 6105–6114. [link].
Touvron, H., Vedaldi, A., Douze, M., et al. (2019). Fixing the train-test resolution discrepancy. In Advances in Neural Information Processing Systems (NeurIPS), volume 32, pages 8250–8260. [link].
Yang, L., Mehta, N., Chen, D., et al. (2020). Resolution adaptive networks for efficient inference. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 2369–2378.
Yap, M. H., Cassidy, B., Pappachan, J. M., et al. (2021). Analysis towards classification of infection and ischaemia of diabetic foot ulcers. In 2021 IEEE EMBS International Conference on Biomedical and Health Informatics (BHI), pages 1–4.
Yun, S., Han, D., Oh, S. J., et al. (2019). CutMix: regularization strategy to train strong classifiers with localizable features. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pages 6023–6032.
Zhang, H., Cisse, M., Dauphin, Y. N., and Lopez-Paz, D. (2018). mixup: beyond empirical risk minimization. In International Conference on Learning Representations (ICLR). [link].
Publicado
08/09/2026
Como Citar
MONTEIRO, Danielle S.; TEIXEIRA, Marcio; CORRÊA, Pedro Pizzigatti.
Green AI e Data Augmentation para Triagem de Úlceras do Pé Diabético no Brasil: um Pipeline Eficiente, Sustentável e Socialmente Orientado. In: DATA SCIENCE FOR SOCIAL GOOD BRAZILIAN WORKSHOP (DS4SG) - SIMPÓSIO BRASILEIRO DE BANCO DE DADOS (SBBD), 41. , 2026, São Carlos/SP.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2026
.
p. 465-472.
DOI: https://doi.org/10.5753/sbbd_estendido.2026.249674.
