Deep Learning for Lung Cancer Histopathological Image Classification: Exploring Data Augmentation and Ensemble Learning Strategies
Resumo
O câncer de pulmão está entre as principais causas de mortalidade. Nesse contexto, a análise de imagens histopatológicas desempenha papel essencial no diagnóstico. Neste trabalho, foram investigadas técnicas de deep learning para a classificação de imagens histopatológicas de câncer de pulmão, combinando estratégias de aumento de dados e ensemble learning. Os resultados demonstraram que essas abordagens contribuíram significativamente para o desempenho dos modelos, alcançando acurácias de até 100% em imagens com ampliação de 20x e 98% em imagens de 40x. Os achados evidenciam o potencial dessas técnicas como ferramentas de apoio à análise histopatológica.Referências
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Borkowski, A. A., Bui, M. M., Thomas, L. B., Wilson, C. P., DeLand, L. A., and Mastorides, S. M. (2019). Lung and colon cancer histopathological image dataset (lc25000). arXiv preprint arXiv:1912.12142.
Cubuk, E. D., Zoph, B., Shlens, J., and Le, Q. V. (2020). Randaugment: Practical automated data augmentation with a reduced search space. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops, pages 702–703.
Diosdado, J., Gilabert, P., Seguí, S., and Borrego, H. (2024). Lunghist700: A dataset of histological images for deep learning in pulmonary pathology. Scientific Data, 11(1):1088.
Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al. (2020). An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929.
Faryna, K., van der Laak, J., and Litjens, G. (2021). Tailoring automated data augmentation to hamp;e-stained histopathology. In Heinrich, M., Dou, Q., de Bruijne, M., Lellmann, J., Schläfer, A., and Ernst, F., editors, Proceedings of the Fourth Conference on Medical Imaging with Deep Learning, volume 143 of Proceedings of Machine Learning Research, pages 168–178. PMLR.
Faryna, K., van der Laak, J., and Litjens, G. (2024). Automatic data augmentation to improve generalization of deep learning in he stained histopathology. Computers in Biology and Medicine, 170:108018.
Gurcan, M. N., Boucheron, L. E., Can, A., Madabhushi, A., Rajpoot, N. M., and Yener, B. (2009). Histopathological image analysis: A review. IEEE reviews in biomedical engineering, 2:147–171.
He, K., Zhang, X., Ren, S., and Sun, J. (2016). Deep residual learning for image recognition. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 770–778.
Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K. Q. (2017). Densely connected convolutional networks. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 4700–4708.
Iarc (2022). Population factsheets - worldwide. [link].
Inca (2022). Estimativa 2023: incidência de câncer no Brasil. INCA, Rio de Janeiro.
Jiang, X. and Xu, C. (2022). Deep learning and machine learning with grid search to predict later occurrence of breast cancer metastasis using clinical data. Journal of clinical medicine, 11(19):5772.
Klangbunrueang, R., Pookduang, P., Chansanam, W., and Lunrasri, T. (2025). Ai-powered lung cancer detection: Assessing vgg16 and cnn architectures for ct scan image classification. Informatics, 12(1):18.
Krishna, S. S. and Reka, S. S. (2025). Early detection of retinopathy of prematurity using voting classifier-based ensemble deep learning models. International Journal of Computational Intelligence Systems, 18(1):238.
Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M., Van Der Laak, J. A., Van Ginneken, B., and Sánchez, C. I. (2017). A survey on deep learning in medical image analysis. Medical image analysis, 42:60–88.
Liu, Z., Mao, H., Wu, C.-Y., Feichtenhofer, C., Darrell, T., and Xie, S. (2022). A convnet for the 2020s. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 11976–11986.
Mienye, I. D. and Sun, Y. (2022). A survey of ensemble learning: Concepts, algorithms, applications, and prospects. IEEE Access, 10:99129–99149.
Myers, D. J. and Wallen, J. M. (2023). Lung adenocarcinoma. In StatPearls [Internet]. StatPearls Publishing.
Relli, V., Trerotola, M., Guerra, E., and Alberti, S. (2019). Abandoning the notion of non-small cell lung cancer. Trends in molecular medicine, 25(7):585–594.
Sammut, C. and Webb, G. I. (2011). Encyclopedia of machine learning. Springer Science & Business Media.
Sarita, Shukla, P. K., Dhaka, V. S., and Jindal, N. (2025). Dual-backbone feature extraction framework for lung cancer classification in histopathology images. International Journal of Information Technology, pages 1–18.
Simonyan, K. and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.
Takahashi, S., Sakaguchi, Y., Kouno, N., Takasawa, K., Ishizu, K., Akagi, Y., Aoyama, R., Teraya, N., Bolatkan, A., Shinkai, N., et al. (2024). Comparison of vision transformers and convolutional neural networks in medical image analysis: a systematic review. Journal of Medical Systems, 48(1):84.
Tan, M. and Le, Q. (2019). Efficientnet: Rethinking model scaling for convolutional neural networks. In International conference on machine learning, pages 6105–6114. PMLR.
Vailankanni, N. and Nagarajan, B. (2026). Deep feature optimization using fusion of multiple self-supervised learning approaches and filter-based feature selection for lung cancer histopathology classification. PloS one, 21(4):e0348194.
Publicado
19/10/2026
Como Citar
SILVA, Joyce M.; VELOSO, Mateus A.; ARAÚJO, D. L.; CARVALHO FILHO, Antonio O. de.
Deep Learning for Lung Cancer Histopathological Image Classification: Exploring Data Augmentation and Ensemble Learning Strategies. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 23. , 2026, Cuiabá/MT.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2026
.
p. 921-932.
ISSN 2763-9061.
DOI: https://doi.org/10.5753/eniac.2026.30649.
