Abstract
Purpose
Diagnosis of breast tumors using histopathological imaging is considered a difficult task. Oncologists may have different opinions on how to use this imaging technique to diagnose tumors. This technique requires classification experience owing to the contrasting appearance caused by tissue preparation, staining processes, and disease heterogeneity. Cancerous breast tissues are classified into malignant and benign tumors according to cell diversity and density. Computer-aided diagnosis (CAD) helps oncologists improve breast tumor diagnosis efficiently and accurately while saving time for early diagnosis.
Methods
Deep learning has begun to evolve in recent decades, and a convolutional neural network (CNN) is used to classify breast histopathological images. The proposed network of three parallel CNN branches (3PCNNB-Net) contains three stages. The first stage consists of three parallel CNN branches with deep residual blocks. In the second stage, we merged the three parallel branches to create a feature fusion path. Then, the fused features were classified in the third stage. The method presented was evaluated by experimenting with the BreakHis database for four magnificent factors.
Results
The BreakHis database was used and implemented. It Contains 7909 images of breast tumor tissues taken from 82 patients. This method achieved 97.14% accuracy. The results were compared with the results of different techniques in the literature.
Conclusion
This study demonstrated a new CNN model for automated breast cancer classification. The method presented successfully classified benign and malignant tumors.
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References
Christian, N. (2018). What to know about breast cancer. Retrieved February 19, 2020, from https://www.medicalnewstoday.com/articles/37136
Li, Y., Wu, J., & Wu, Q. (2019). Classification of breast cancer histology images using multi-size and discriminative patches based on deep learning. IEEE Access, 7, 21400–21408. https://doi.org/10.1109/ACCESS.2019.2898044
He, L., Long, L. R., Antani, S., & Thoma, G. R. (2012). Histology image analysis for carcinoma detection and grading. Computer Methods and Programs in Biomedicine, 107(3), 538–556. https://doi.org/10.1016/j.cmpb.2011.12.007
Han, Z., Wei, B., Zheng, Y., Yin, Y., Li, K., & Li, S. (2017). Breast cancer multi-classification from histopathological images with structured deep learning model. Scientific Reports, 7(1), 4172. https://doi.org/10.1038/s41598-017-04075-z
Shen, D., Wu, G., & Suk, H.-I. (2017). Deep learning in medical image analysis. Annual Review of Biomedical Engineering, 19, 221–248. https://doi.org/10.1146/annurev-bioeng-071516-044442
Zheng, Y., Yang, C., & Wang, H. (2020). Enhancing breast cancer detection with recurrent neural network (Vol. 11399, SPIE Defense + Commercial Sensing): SPIE
Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning representations by back-propagating errors. Nature, 323(6088), 533–536. https://doi.org/10.1038/323533a0
Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Journal of Neural Computation, 9, 1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017). ImageNet classification with deep convolutional neural networks. 60(6 %J Commun. ACM), 84–90, doi:https://doi.org/10.1145/3065386.
Szegedy, C., Wei, L., Yangqing, J., Sermanet, P., Reed, S., … Anguelov, D, Going deeper with convolutions. In 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 7–12 June 2015 (pp. 1–9). doi:https://doi.org/10.1109/CVPR.2015.7298594
He, K., Zhang, X., Ren, S., & Sun, J. Deep residual learning for image recognition. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 27–30 June 2016 (pp. 770–778). doi:https://doi.org/10.1109/CVPR.2016.90
Simonyan, K., & Zisserman, A. J. (2014). Very deep convolutional networks for large-scale image recognition
Motlagh, M. H., Jannesari, M., Aboulkheyr, H., Khosravi, P., Elemento, O., Totonchi, M., … Hajirasouliha, I. (2018). Breast cancer histopathological image classification: A deep learning Approach. bioRxiv, doi: https://doi.org/10.1101/242818
Spanhol, F. A., Oliveira, L. S., Petitjean, C., & Heutte, L. (2016). A dataset for breast cancer histopathological image classification. IEEE Transactions on Biomedical Engineering, 63(7), 1455–1462. https://doi.org/10.1109/TBME.2015.2496264
Feng, Y., Zhang, L., & Mo, J. (2020). Deep manifold preserving autoencoder for classifying breast cancer histopathological images. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 17(1), 91–101. https://doi.org/10.1109/TCBB.2018.2858763
Nahid, A.-A., Mehrabi, M. A., & Kong, Y. (2018). Histopathological breast cancer image classification by deep neural network techniques guided by local clustering. BioMed Research International, 2018, 2362108–2362108. https://doi.org/10.1155/2018/2362108
Chang, J., Yu, J., Han, T., Chang, H., & Park, E. A method for classifying medical images using transfer learning: A pilot study on histopathology of breast cancer. In 2017 IEEE 19th International Conference on e-Health Networking, Applications and Services (Healthcom), 12–15 Oct. 2017 (pp. 1–4). doi:https://doi.org/10.1109/HealthCom.2017.8210843.
Touahri, R., AzizI, N., Hammami, N. E., Aldwairi, M., & Benaida, F. Automated breast tumor diagnosis using local binary patterns (LBP) based on deep learning classification. In 2019 International Conference on Computer and Information Sciences (ICCIS), 3–4 April 2019 (pp. 1–5). doi:https://doi.org/10.1109/ICCISci.2019.8716428.
Ismail, N. S., & Sovuthy, C. Breast cancer detection based on deep learning technique. In 2019 International UNIMAS STEM 12th Engineering Conference (EnCon), 28–29 Aug. 2019 (pp. 89–92). doi:https://doi.org/10.1109/EnCon.2019.8861256.
Khuriwal, N., & Mishra, N. Breast cancer detection from histopathological images using deep learning. In 2018 3rd International Conference and Workshops on Recent Advances and Innovations in Engineering (ICRAIE), 22–25 Nov. 2018 (pp. 1–4). doi:https://doi.org/10.1109/ICRAIE.2018.8710426.
Xiao, Y., Wu, J., Lin, Z., & Zhao, X. Breast cancer diagnosis using an unsupervised feature Extraction algorithm based on deep learning. In 2018 37th Chinese Control Conference (CCC), 25–27 July 2018 (pp. 9428–9433). doi:https://doi.org/10.23919/ChiCC.2018.8483140.
Shen, L., Margolies, L. R., Rothstein, J. H., Fluder, E., McBride, R., & Sieh, W. (2019). Deep learning to improve breast cancer detection on screening mammography. Scientific Reports, 9(1), 12495. https://doi.org/10.1038/s41598-019-48995-4
Wu, N., Phang, J., Park, J., Shen, Y., Huang, Z., Zorin, M., et al. (2019). Deep neural networks improve radiologists’ performance in breast cancer screening. IEEE Transactions on Medical Imaging. https://doi.org/10.1109/TMI.2019.2945514
Nazeri, K., Aminpour, A., & Ebrahimi, M. Two-stage convolutional neural network for breast cancer histology image classification. In A. Campilho, F. Karray, B. T. H. Romeny (Eds.), Image Analysis and Recognition, Cham, 2018 (pp. 717–726): Springer: New York
Mekha, P., & Teeyasuksaet, N. Deep learning algorithms for predicting breast cancer based on tumor cells. In 2019 Joint International Conference on Digital Arts, Media and Technology with ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications Engineering (ECTI DAMT-NCON), 30 Jan–2 Feb. 2019 (pp. 343–346). doi:https://doi.org/10.1109/ECTI-NCON.2019.8692297.
Veta, M., Pluim, J. P., Van Diest, P. J., & Viergever, M. A. (2014). Breast cancer Histopathology image analysis: A review. IEEE Transactions on Biomedical Engineering, 61(5), 1400–1411. https://doi.org/10.1109/TBME.2014.2303852
Nahid, A., & Kong, Y. (2017). Involvement of machine learning for breast cancer image classification: A survey. Computational and Mathematical Methods in Medicine, 2017, 1–29. https://doi.org/10.1155/2017/3781951
Abd-Ellah, M. K., Awad, A. I., Hamed, H. F. A., & Khalaf, A. A. M. Parallel deep CNN structure for glioma detection and classification via brain MRI images. In 2019 31st International Conference on Microelectronics (ICM), 15–18 Dec. 2019 (pp. 304–307). doi:https://doi.org/10.1109/ICM48031.2019.9021872.
Abd-Ellah, M. K., Khalaf, A. A., Awad, A. I., & Hamed, H. F. (2019). TPUAR-Net: two parallel U-Net with asymmetric residual-based deep convolutional neural network for brain tumor segmentation. In F. Karray, A. Campilho, & A. Yu (Eds.), International conference on image analysis and recognition (pp. 106–116). Springer.
Abd-Ellah, M. K., Awad, A. I., Khalaf, A. A., & Hamed, H. F. (2020). Deep Convolutional neural networks: foundations and applications in medical imaging. Deep learning in computer vision (pp. 233–260). CRC Press.
Abd-Ellah, M. K., Awad, A. I., Khalaf, A. A. M., & Hamed, H. F. A. (2018). Two-phase multi-model automatic brain tumour diagnosis system from magnetic resonance images using convolutional neural networks. EURASIP Journal on Image and Video Processing, 2018(1), 97. https://doi.org/10.1186/s13640-018-0332-4
Nandini, G. S., Kumar, A. S., & Chidananda, K. (2021). Dropout technique for image classification based on extreme learning machine. Global Transitions Proceedings, 2(1), 111–116. https://doi.org/10.1016/j.gltp.2021.01.015
Reghunath, A., Nair, S. V., & Shah, J. Deep learning based customized model for features extraction. In 2019 International Conference on Communication and Electronics Systems (ICCES), 17–19 July 2019 (pp. 1406-1411). doi:https://doi.org/10.1109/ICCES45898.2019.9002299.
Rączkowski, Ł, Możejko, M., Zambonelli, J., & Szczurek, E. (2019). ARA: accurate, reliable and active histopathological image classification framework with Bayesian deep learning. Scientific Reports, 9(1), 14347. https://doi.org/10.1038/s41598-019-50587-1
Ibraheem, A. M., Rahouma, K. H., & Hamed, H. F. A. Automatic MRI breast tumor detection using discrete wavelet transform and support vector machines. In 2019 Novel Intelligent and Leading Emerging Sciences Conference (NILES), 28–30 Oct. 2019 (Vol. 1, pp. 88–91). doi:https://doi.org/10.1109/NILES.2019.8909345
Qi, Q., Li, Y., Wang, J., Zheng, H., Huang, Y., Ding, X., et al. (2019). Label-efficient breast cancer histopathological image classification. IEEE Journal of Biomedical and Health Informatics, 23(5), 2108–2116. https://doi.org/10.1109/JBHI.2018.2885134
Spanhol, F. A., Oliveira, L. S., Petitjean, C., & Heutte, L. Breast cancer histopathological image classification using Convolutional Neural Networks. In 2016 International Joint Conference on Neural Networks (IJCNN), 24–29 July 2016 (pp. 2560–2567). doi:https://doi.org/10.1109/IJCNN.2016.7727519
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Ibraheem, A.M., Rahouma, K.H. & Hamed, H.F.A. 3PCNNB-Net: Three Parallel CNN Branches for Breast Cancer Classification Through Histopathological Images. J. Med. Biol. Eng. 41, 494–503 (2021). https://doi.org/10.1007/s40846-021-00620-4
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DOI: https://doi.org/10.1007/s40846-021-00620-4