EfficientXYZ-DeepFeatures: Color scheme selection and Deep Features architecture in colon cancer classification from histopathological images
Abstract
Colon cancer classification in histopathological images poses a significant challenge, requiring computational methods to assist experts in pattern identification. This paper proposes an innovative method by automating the selection of the color scheme and identifying the most efficient neural network architecture for Deep Features extraction. The method demonstrated that the XYZ color scheme provides the best representation, and EfficientNetB0 for Deep Features extraction. The best results show an accuracy of 99.33%, sensitivity of 99.31%, specificity of 99.35%, and an F1-Score of 99.35%. Thus, the importance of automated selection of color scheme and architecture for histopathological analyses is emphasized.
References
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Rajinikanth, V., Kadry, S., Mohan, R., Rama, A., Khan, M. A., and Kim, J. (2023). Colon histology slide classification with deep-learning framework using individual and fused features. Mathematical Biosciences and Engineering, 20(11):19454–19467.
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Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z. (2016). Rethinking the inception architecture for computer vision. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 2818–2826.
Tan, M. and Le, Q. (2019). Efficientnet: Rethinking model scaling for convolutional neural networks. In International conference on machine learning, page 6105. PMLR.
Van der Laak, J., Litjens, G., and Ciompi, F. (2021). Deep learning in histopathology: the path to the clinic. Nature medicine, 27(5):775–784.
Bukhari, S. U. K., Syed, A., Bokhari, S. K. A., Hussain, S. S., Armaghan, S. U., and Shah, S. S. H. (2020). The histological diagnosis of colonic adenocarcinoma by applying partial self supervised learning. MedRxiv, pages 2020–08.
Carvalho, E. D., Antônio Filho, O., Silva, R. R., Araújo, F. H., Diniz, J. O., Silva, A. C., Paiva, A. C., and Gattass, M. (2020). Breast cancer diagnosis from histopathological images using textural features and cbir. Artificial Intelligence in Medicine, 105:101845.
de Oliveira Santos, M., de Lima, F. C. d. S., Martins, L. F. L., Oliveira, J. F. P., de Almeida, L. M., and de Camargo Cancela, M. (2023). Estimativa de incidência de câncer no brasil, 2023-2025. Revista Brasileira de Cancerologia, 69(1).
Deo, S., Sharma, J., and Kumar, S. (2022). Globocan 2020 report on global cancer burden: challenges and opportunities for surgical oncologists. Annals of surgical oncology, 29(11):6497–6500.
Diniz, J., Quintanilha, D., Filho, A. C., Jr, D. G., Silva, A., Jr, G. B., Paiva, A., and Luz, D. (2023). Detecção de covid-19 em imagens de raio-x de tórax através de seleção automática de pré-processamento e de rede neural convolucional. In Anais do XXIII Simpósio Brasileiro de Computação Aplicada à Saúde, pages 162–173, Porto Alegre, RS, Brasil. SBC.
Diniz, J. O. B., Ferreira, J. L., Cortes, O. A. C., Silva, A. C., and de Paiva, A. C. (2022). An automatic approach for heart segmentation in ct scans through image processing techniques and concat-u-net. Expert Systems with Applications, 196:116632.
Figueredo, W., Silva, I., Diniz, J., Silva, A., Paiva, A., Salomão, A., and Oliveira, M. (2023). Abordagem computacional baseada em deep learning para o diagnóstico de endometriose profunda através de imagens de ressonância magnética. In Anais do XXIII Simpósio Brasileiro de Computação Aplicada à Saúde, pages 138–149, Porto Alegre, RS, Brasil. SBC.
Gonzalez, R. and Woods, R. (2008). Digital image processing. Pearson, Prentice Hall.
Hamida, A. B., Devanne, M., Weber, J., Truntzer, C., Derangère, V., Ghiringhelli, F., Forestier, G., and Wemmert, C. (2021). Deep learning for colon cancer histopathological images analysis. Computers in Biology and Medicine, 136:104730.
He, K., Zhang, X., Ren, S., and Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 770–778.
Hidayah, N., Ramadanti, A. N., and Novitasari, D. C. R. (2023). Classification of colon cancer based on hispathological images using adaptive neuro fuzzy inference system (anfis). Khazanah Informatika: Jurnal Ilmu Komputer dan Informatika, 9(2):162–168.
Howard, A., Sandler, M., Chu, G., Chen, L.-C., Chen, B., Tan, M., Wang, W., Zhu, Y., Pang, R., Vasudevan, V., et al. (2019). Searching for mobilenetv3. In Proceedings of the IEEE/CVF international conference on computer vision, pages 1314–1324.
Júnior, D. A. D., da Cruz, L. B., Diniz, J. O. B., da Silva, G. L. F., Junior, G. B., Silva, A. C., de Paiva, A. C., Nunes, R. A., and Gattass, M. (2021). Automatic method for classifying covid-19 patients based on chest x-ray images, using deep features and pso-optimized xgboost. Expert Systems with Applications, 183:115452.
Júnior, D. D., Cruz, L., Diniz, J., Júnior, G. B., and Silva, A. (2021). Classificação automática de glóbulos brancos usando descritores de forma e textura e extreme gradient boosting. In Anais do XXI Simpósio Brasileiro de Computação Aplicada à Saúde, pages 95–106, Porto Alegre, RS, Brasil. SBC.
Kuepper, C., Großerueschkamp, F., Kallenbach-Thieltges, A., Mosig, A., Tannapfel, A., and Gerwert, K. (2016). Label-free classification of colon cancer grading using infrared spectral histopathology. Faraday discussions, 187:105–118.
Mangal, S., Chaurasia, A., and Khajanchi, A. (2020). Convolution neural networks for diagnosing colon and lung cancer histopathological images. arXiv preprint arXiv:2009.03878.
Matos, C., Oliveira, M., Diniz, J., Fernandes, A., Junior, G. B., and Paiva, A. (2023). Ppm-deeplab: Módulo de pirâmide de pooling como codificador da rede deeplabv3+ para segmentação de rins, cistos e tumores renais. In Anais do XXIII Simpósio Brasileiro de Computação Aplicada à Saúde, pages 210–221, Porto Alegre, RS, Brasil. SBC.
Rajinikanth, V., Kadry, S., Mohan, R., Rama, A., Khan, M. A., and Kim, J. (2023). Colon histology slide classification with deep-learning framework using individual and fused features. Mathematical Biosciences and Engineering, 20(11):19454–19467.
Santos, P., Brito, V., Filho, A. C., Sousa, A., Diniz, J., and Luz, D. (2023). Efficientbacillus: uma arquitetura profunda para detecção dos bacilos de koch. In Anais do XXIII Simpósio Brasileiro de Computação Aplicada à Saúde, pages 198–209, Porto Alegre, RS, Brasil. SBC.
Simonyan, K. and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.
Swarna, I. J. and Hashi, E. K. (2023). Detection of colon cancer using inception v3 and ensembled cnn model. In 2023 International Conference on Electrical, Computer and Communication Engineering (ECCE), pages 1–6. IEEE.
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z. (2016). Rethinking the inception architecture for computer vision. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 2818–2826.
Tan, M. and Le, Q. (2019). Efficientnet: Rethinking model scaling for convolutional neural networks. In International conference on machine learning, page 6105. PMLR.
Van der Laak, J., Litjens, G., and Ciompi, F. (2021). Deep learning in histopathology: the path to the clinic. Nature medicine, 27(5):775–784.
Published
2024-06-25
How to Cite
DINIZ, João O. B.; RIBEIRO, Neilson P.; DIAS JUNIOR, Domingos A.; CRUZ, Luana B. da; CARVALHO FILHO, Antonio O. de; GOMES JR, Daniel L.; SILVA, Aristófanes C.; PAIVA, Anselmo C. de.
EfficientXYZ-DeepFeatures: Color scheme selection and Deep Features architecture in colon cancer classification from histopathological images. In: BRAZILIAN SYMPOSIUM ON COMPUTING APPLIED TO HEALTH (SBCAS), 24. , 2024, Goiânia/GO.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 82-93.
ISSN 2763-8952.
DOI: https://doi.org/10.5753/sbcas.2024.1887.
