Efficient Breast Cancer Classification Using Histopathological Images and a Simpler VGG
Resumo
Breast cancer is the second most deadly disease worldwide. This severe condition led 627,000 people to die in 2018. Thus, early detection is critical for improving the patients' lifetime or even cure them. In this context, we can appeal to Medicine 4.0 that exploits the machine learning capabilities to obtain a faster and more efficient diagnosis. Therefore, this work aims to apply a simpler convolutional neural network, called VGG-7, for classifying breast cancer in histopathological images. Results have shown that VGG-7 overcomes the performance of VGG-16 and VGG-19, showing an accuracy of 98%, a precision of 99%, a recall of 98%, and an F1 score of 98%.
Palavras-chave:
Breast Cancer, Convolutional Neural Networks, Classification, VGG, Transfer Learning
Referências
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Khamparia, A., Bharati, A., Podder, P., Gupta, D., Khanna, A., K., P. T., and Thanh, D. N. H. (2021). Diagnosis of breast cancer based on modern mammography using hybrid transfer learning. Multidimensional Systems and Signal Processing, 32:747–765.
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Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau,D., Brucher, M., Perrot, M., and Duchesnay, E. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12:2825–2830.
Saikia, A. R., Bora, K., Mahanta, L. B., and Kumar Das, A. (2019). Comparative assessment of CNN architectures for classification of breast fnac images. Tissue and Cell, 57:8–14. EM in cell and tissues.
Shallu and Mehra, R. (2018). Breast cancer histology images classification: Training from scratch or transfer learning? ICT Express, 4(4):247–254.
Simonyan, K. and Zisserman, A. (2015). Very deep convolutional networks for large-scale image recognition. In International Conference on Learning Representations.
Singh, R., Ahmed, T., Kumar, A., Singh, A. K., Pandey, A. K., and Singh, S. K. (2020). Imbalanced breast cancer classification using transfer learning. IEEE/ACM Transac tions on Computational Biology and Bioinformatics, pages 83–93.
Sitaula, C. and Hossain, M. (2020). Attention-based VGG-16 model for covid-19 chest x-ray image classification. Applied Intelligence, 51:2850–2863.
Spanhol, F. A., Oliveira, L. S., Petitjean, C., and Heutte, L. (2016a). Breast cancer histopathological image classification using convolutional neural networks. In 2016 International Joint Conference on Neural Networks (IJCNN), pages 2560–2567.
Spanhol, F. A., Oliveira, L. S., Petitjean, C., and Heutte, L. (2016b). A dataset for breast cancer histopathological image classification. IEEE Transactions on Biomedical Engineering, 63(7):1455–1462.
Titoriya, A. and Sachdeva, S. (2019). Breast cancer histopathology image classification using AlexNet. In 4th International Conference on Information Systems and Computer Networks (ISCON), pages 708–712.
WHO (2020). World Health Organization, Breast cancer. https://www.who.int/cancer/prevention/diagnosis-screening/breast-cancer/en/, Visit on 05-31-2020.
Wolf, B. and Scholze, C. (2017). “medicine 4.0”. Current Directions in Biomedical Engineering, 3(2):183–186.
AICR (2020). American Institute for Cancer Research, Breast cancer statisitics. https://www.wcrf.org/dietandcancer/cancer-trends/breast-cancer-statistics, Visit on 05-31-2020
Dabeer, S., Khan, M. M., and Islam, S. (2019). Cancer diagnosis in histopathological image: CNN based approach. Informatics in Medicine Unlocked, 16:100231.
Glorot, X. and Bengio, Y. (2010). Understanding the difficulty of training deep feedforward neural networks. Journal of Machine Learning Research - Proceedings Track, 9:249–256.
Google (2020). Google Colaboratory. https://colab.research.google.com/notebooks/intro.ipynb, Visit on 06-15-2020.
Gulshan, V., Peng, L., Coram, M., Stumpe, M. C., Wu, D., Narayanaswamy, A., Venu gopalan, S., Widner, K., Madams, T., Cuadros, J., Kim, R., Raman, R., Nelson, P. C., Mega, J. L., and Webster, D. R. (2016). Development and Validation of a Deep Learn ing Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs. JAMA, 316(22):2402–2410.
INCA (2016). Ministerio da Saúde - Instituto Nacional de Câncer, Câncer de mama: vamos falar sobre isso? https://www.inca.gov.br/sites/ufu.sti.inca.local/files//media/document//cartilha cancer-de-mama-vamos-falar-sobre -isso2016.pdf, Visit on 05-31-202
Ismail, N. S. and Sovuthy, C. (2019). Breast cancer detection based on deep learning tech nique. In 2019 International UNIMAS STEM 12th Engineering Conference (EnCon), pages 89–92.
Khamparia, A., Bharati, A., Podder, P., Gupta, D., Khanna, A., K., P. T., and Thanh, D. N. H. (2021). Diagnosis of breast cancer based on modern mammography using hybrid transfer learning. Multidimensional Systems and Signal Processing, 32:747–765.
Kingma, D. and Ba, J. (2014). Adam: A method for stochastic optimization. International Conference on Learning Representation
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau,D., Brucher, M., Perrot, M., and Duchesnay, E. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12:2825–2830.
Saikia, A. R., Bora, K., Mahanta, L. B., and Kumar Das, A. (2019). Comparative assessment of CNN architectures for classification of breast fnac images. Tissue and Cell, 57:8–14. EM in cell and tissues.
Shallu and Mehra, R. (2018). Breast cancer histology images classification: Training from scratch or transfer learning? ICT Express, 4(4):247–254.
Simonyan, K. and Zisserman, A. (2015). Very deep convolutional networks for large-scale image recognition. In International Conference on Learning Representations.
Singh, R., Ahmed, T., Kumar, A., Singh, A. K., Pandey, A. K., and Singh, S. K. (2020). Imbalanced breast cancer classification using transfer learning. IEEE/ACM Transac tions on Computational Biology and Bioinformatics, pages 83–93.
Sitaula, C. and Hossain, M. (2020). Attention-based VGG-16 model for covid-19 chest x-ray image classification. Applied Intelligence, 51:2850–2863.
Spanhol, F. A., Oliveira, L. S., Petitjean, C., and Heutte, L. (2016a). Breast cancer histopathological image classification using convolutional neural networks. In 2016 International Joint Conference on Neural Networks (IJCNN), pages 2560–2567.
Spanhol, F. A., Oliveira, L. S., Petitjean, C., and Heutte, L. (2016b). A dataset for breast cancer histopathological image classification. IEEE Transactions on Biomedical Engineering, 63(7):1455–1462.
Titoriya, A. and Sachdeva, S. (2019). Breast cancer histopathology image classification using AlexNet. In 4th International Conference on Information Systems and Computer Networks (ISCON), pages 708–712.
WHO (2020). World Health Organization, Breast cancer. https://www.who.int/cancer/prevention/diagnosis-screening/breast-cancer/en/, Visit on 05-31-2020.
Wolf, B. and Scholze, C. (2017). “medicine 4.0”. Current Directions in Biomedical Engineering, 3(2):183–186.
Publicado
18/07/2021
Como Citar
RODRIGUES FILHO, Marcelo Luis; CORTES, Omar Andres Carmona.
Efficient Breast Cancer Classification Using Histopathological Images and a Simpler VGG. In: BRAZILIAN E-SCIENCE WORKSHOP (BRESCI), 15. , 2021, Evento Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2021
.
p. 9-16.
ISSN 2763-8774.
DOI: https://doi.org/10.5753/bresci.2021.15783.
