Oral Squamous Cell Carcinoma Detection: A Comparison of CNNs in Histopathological Images
Resumo
Oral Squamous Cell Carcinoma (OSCC) is the most prevalent form of oral cancer, accounting for approximately 90% of cases worldwide. Early and accurate diagnosis is critical for improving patient outcomes, yet traditional histopathological analysis remains subjective and time-consuming. This study presents a systematic benchmark comparing the performance of eleven convolutional neural network (CNN) architectures for the automated classification of OSCC in histopathological images. Using a publicly available dataset, all models were trained and evaluated under uniform conditions. Metrics such as accuracy, recall, precision, F1-score, and AUC-ROC were employed to assess performance. Results demonstrated that EfficientNetB3 achieved the highest accuracy (94.12%) and overall robustness, outperforming other architectures. This study provides a comprehensive comparative analysis of CNNs for OSCC detection, highlighting the potential of modern architectures like EfficientNet to enhance diagnostic precision. The standardized benchmark established here facilitates future research and clinical integration of deep learning tools for histopathological cancer diagnosis.
Referências
A. S. R. C. Murthy, G. Mercy, L. J. Prakash, and K. S. Bose, “Histopath-dl-oc: Deep learning for oral cancer prediction from histopathology data,” in 2025 International Conference on Inventive Computation Technologies (ICICT). IEEE, 2025, pp. 1190–1197.
WHO, “World health organization: Oral cancer,” [link], 2024, accessed on: June 26. 2025.
INCA, “Instituto nacional de câncer, estimativa 2023: Incidência de câncer no brasil,” [link], 2023, accessed on: June 26. 2025.
A. Kumar and L. Nelson, “Enhancing oral squamous cell carcinoma detection using efficientnetb3 from histopathologic images,” in 2025 International Conference on Multi-Agent Systems for Collaborative Intelligence (ICMSCI). IEEE, 2025, pp. 950–956.
S. R. Bisht, P. Mishra, D. Yadav, R. Rawal, and K. P. Mercado-Shekhar, “Current and emerging techniques for oral cancer screening and diagnosis: a review,” Progress in Biomedical Engineering, vol. 3, no. 4, p. 042003, 2021.
G. Litjens, T. Kooi, B. E. Bejnordi, A. A. A. Setio, F. Ciompi, M. Ghafoorian, J. A. Van Der Laak, B. Van Ginneken, and C. I. Sánchez, “A survey on deep learning in medical image analysis,” Medical image analysis, vol. 42, pp. 60–88, 2017.
E. D. Carvalho, O. Antonio Filho, R. R. Silva, F. H. Araujo, J. O. Diniz, A. C. Silva, A. C. Paiva, and M. Gattass, “Breast cancer diagnosis from histopathological images using textural features and cbir,” Artificial intelligence in medicine, vol. 105, p. 101845, 2020.
N. P. Ribeiro, F. R. Teles, J. O. B. Diniz, L. B. da Cruz, D. A. Dias Jr, G. Braz Junior, J. D. de Almeida, and A. C. de Paiva, “Improving colorectal cancer diagnosis using mirnet and inceptionv3 on histopathological images,” in Brazilian Conference on Intelligent Systems. Springer, 2024, pp. 321–334.
J. O. Diniz, N. P. Ribeiro, D. A. D. Junior, L. B. da Cruz, A. O. de Carvalho Filho, D. L. Gomes Jr, A. C. Silva, and A. C. de Paiva, “Efficientxyz-deepfeatures: seleção de esquema de cor e arquitetura deep features na classificação de câncer de cólon em imagens histopatológicas,” in Simpósio Brasileiro de Computação Aplicada à Saúde (SBCAS). SBC, 2024, pp. 82–93.
B. M. S. Maia, M. C. F. R. de Assis, L. M. de Lima, M. B. Rocha, H. G. Calente, M. L. A. Correa, D. R. Camisasca, and R. A. Krohling, “Transformers, convolutional neural networks, and few-shot learning for classification of histopathological images of oral cancer,” Expert Systems with Applications, vol. 241, p. 122418, 2024.
M. Das, R. Dash, S. K. Mishra, and A. K. Dalai, “An ensemble deep learning model for oral squamous cell carcinoma detection using histopathological image analysis,” IEEE Access, 2024.
D. Raval, A. Patel, J. N. Undavia, A. Shukla, and U. Patel, “Oral cancer detection with convolutional neural networks and transfer learning: A resnet-based approach,” in International Conference on Data Analytics & Management. Springer, 2024, pp. 237–245.
B. A. Tamanini, V. G. Sousa, L. P. Rodrigues, D. M. Oliveira, C. X. Dias, L. B. da Cruz, J. O. Diniz, and L. O. S. Júnior, “Classificaçao de carcinoma endometrioide de ovário por transformaçao de esquema de cor e radiomics em imagens histopatológicas,” in Simpósio Brasileiro de Computação Aplicada à Saúde (SBCAS). SBC, 2025, pp. 68–79.
M. A. Deif, H. Attar, A. Amer, I. A. Elhaty, M. R. Khosravi, and A. A. Solyman, “Diagnosis of oral squamous cell carcinoma using deep neural networks and binary particle swarm optimization on histopathological images: an aiomt approach,” Computational Intelligence and Neuroscience, vol. 2022, no. 1, p. 6364102, 2022.
A.-u. Rahman, A. Alqahtani, N. Aldhafferi, M. U. Nasir, M. F. Khan, M. A. Khan, and A. Mosavi, “Histopathologic oral cancer prediction using oral squamous cell carcinoma biopsy empowered with transfer learning,” Sensors, vol. 22, no. 10, p. 3833, 2022.
R. L. d. Prado, J. A. Marsicano, A. K. Frois, and J. D. Brancher, “The use of machine learning to support the diagnosis of oral alterations,” Pesquisa Brasileira em Odontopediatria e Clínica Integrada, vol. 25, p. e240048, 2025.
A. F. Kebede, “Histopathologic oral cancer detection using cnns,” [link], 2022, accessed on: June 26. 2025.
D. A. D. Júnior, L. B. da Cruz, J. O. B. Diniz, G. L. F. da Silva, G. B. Junior, A. C. Silva, A. C. de Paiva, R. A. Nunes, and M. Gattass, “Automatic method for classifying covid-19 patients based on chest x-ray images, using deep features and pso-optimized xgboost,” Expert Systems with Applications, vol. 183, p. 115452, 2021.
J. O. B. Diniz, N. P. Ribeiro, D. A. Dias Jr, L. B. da Cruz, G. L. da Silva, D. L. Gomes Jr, A. C. de Paiva, and A. C. Silva, “Anisotropicbreast-vit: Breast cancer classification in ultrasound images using anisotropic filtering and vision transformer,” in Brazilian Conference on Intelligent Systems. Springer, 2024, pp. 95–109.
F. Chollet, “Xception: Deep learning with depthwise separable convolutions,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2017, pp. 1251–1258.
D. M. Powers, “Evaluation: from precision, recall and f-measure to roc, informedness, markedness and correlation,” arXiv preprint arXiv:2010.16061, 2020.
WHO, “World health organization: Oral cancer,” [link], 2024, accessed on: June 26. 2025.
INCA, “Instituto nacional de câncer, estimativa 2023: Incidência de câncer no brasil,” [link], 2023, accessed on: June 26. 2025.
A. Kumar and L. Nelson, “Enhancing oral squamous cell carcinoma detection using efficientnetb3 from histopathologic images,” in 2025 International Conference on Multi-Agent Systems for Collaborative Intelligence (ICMSCI). IEEE, 2025, pp. 950–956.
S. R. Bisht, P. Mishra, D. Yadav, R. Rawal, and K. P. Mercado-Shekhar, “Current and emerging techniques for oral cancer screening and diagnosis: a review,” Progress in Biomedical Engineering, vol. 3, no. 4, p. 042003, 2021.
G. Litjens, T. Kooi, B. E. Bejnordi, A. A. A. Setio, F. Ciompi, M. Ghafoorian, J. A. Van Der Laak, B. Van Ginneken, and C. I. Sánchez, “A survey on deep learning in medical image analysis,” Medical image analysis, vol. 42, pp. 60–88, 2017.
E. D. Carvalho, O. Antonio Filho, R. R. Silva, F. H. Araujo, J. O. Diniz, A. C. Silva, A. C. Paiva, and M. Gattass, “Breast cancer diagnosis from histopathological images using textural features and cbir,” Artificial intelligence in medicine, vol. 105, p. 101845, 2020.
N. P. Ribeiro, F. R. Teles, J. O. B. Diniz, L. B. da Cruz, D. A. Dias Jr, G. Braz Junior, J. D. de Almeida, and A. C. de Paiva, “Improving colorectal cancer diagnosis using mirnet and inceptionv3 on histopathological images,” in Brazilian Conference on Intelligent Systems. Springer, 2024, pp. 321–334.
J. O. Diniz, N. P. Ribeiro, D. A. D. Junior, L. B. da Cruz, A. O. de Carvalho Filho, D. L. Gomes Jr, A. C. Silva, and A. C. de Paiva, “Efficientxyz-deepfeatures: seleção de esquema de cor e arquitetura deep features na classificação de câncer de cólon em imagens histopatológicas,” in Simpósio Brasileiro de Computação Aplicada à Saúde (SBCAS). SBC, 2024, pp. 82–93.
B. M. S. Maia, M. C. F. R. de Assis, L. M. de Lima, M. B. Rocha, H. G. Calente, M. L. A. Correa, D. R. Camisasca, and R. A. Krohling, “Transformers, convolutional neural networks, and few-shot learning for classification of histopathological images of oral cancer,” Expert Systems with Applications, vol. 241, p. 122418, 2024.
M. Das, R. Dash, S. K. Mishra, and A. K. Dalai, “An ensemble deep learning model for oral squamous cell carcinoma detection using histopathological image analysis,” IEEE Access, 2024.
D. Raval, A. Patel, J. N. Undavia, A. Shukla, and U. Patel, “Oral cancer detection with convolutional neural networks and transfer learning: A resnet-based approach,” in International Conference on Data Analytics & Management. Springer, 2024, pp. 237–245.
B. A. Tamanini, V. G. Sousa, L. P. Rodrigues, D. M. Oliveira, C. X. Dias, L. B. da Cruz, J. O. Diniz, and L. O. S. Júnior, “Classificaçao de carcinoma endometrioide de ovário por transformaçao de esquema de cor e radiomics em imagens histopatológicas,” in Simpósio Brasileiro de Computação Aplicada à Saúde (SBCAS). SBC, 2025, pp. 68–79.
M. A. Deif, H. Attar, A. Amer, I. A. Elhaty, M. R. Khosravi, and A. A. Solyman, “Diagnosis of oral squamous cell carcinoma using deep neural networks and binary particle swarm optimization on histopathological images: an aiomt approach,” Computational Intelligence and Neuroscience, vol. 2022, no. 1, p. 6364102, 2022.
A.-u. Rahman, A. Alqahtani, N. Aldhafferi, M. U. Nasir, M. F. Khan, M. A. Khan, and A. Mosavi, “Histopathologic oral cancer prediction using oral squamous cell carcinoma biopsy empowered with transfer learning,” Sensors, vol. 22, no. 10, p. 3833, 2022.
R. L. d. Prado, J. A. Marsicano, A. K. Frois, and J. D. Brancher, “The use of machine learning to support the diagnosis of oral alterations,” Pesquisa Brasileira em Odontopediatria e Clínica Integrada, vol. 25, p. e240048, 2025.
A. F. Kebede, “Histopathologic oral cancer detection using cnns,” [link], 2022, accessed on: June 26. 2025.
D. A. D. Júnior, L. B. da Cruz, J. O. B. Diniz, G. L. F. da Silva, G. B. Junior, A. C. Silva, A. C. de Paiva, R. A. Nunes, and M. Gattass, “Automatic method for classifying covid-19 patients based on chest x-ray images, using deep features and pso-optimized xgboost,” Expert Systems with Applications, vol. 183, p. 115452, 2021.
J. O. B. Diniz, N. P. Ribeiro, D. A. Dias Jr, L. B. da Cruz, G. L. da Silva, D. L. Gomes Jr, A. C. de Paiva, and A. C. Silva, “Anisotropicbreast-vit: Breast cancer classification in ultrasound images using anisotropic filtering and vision transformer,” in Brazilian Conference on Intelligent Systems. Springer, 2024, pp. 95–109.
F. Chollet, “Xception: Deep learning with depthwise separable convolutions,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2017, pp. 1251–1258.
D. M. Powers, “Evaluation: from precision, recall and f-measure to roc, informedness, markedness and correlation,” arXiv preprint arXiv:2010.16061, 2020.
Publicado
30/09/2025
Como Citar
DINIZ, João O. B.; TAMINI, Breno A.; ALEVINO, André F.; SOUZA JR., Luiz O. O.; CRUZ, Luana Batista da.
Oral Squamous Cell Carcinoma Detection: A Comparison of CNNs in Histopathological Images. In: WORKSHOP ON DIGITAL AND COMPUTATIONAL PATHOLOGY - CONFERENCE ON GRAPHICS, PATTERNS AND IMAGES (SIBGRAPI), 38. , 2025, Salvador/BA.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
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p. 369-372.
