International Journal of Computer Science & Network Security

International Journal of Computer Science & Network Security (국제컴퓨터통신보호논문지학회)
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A Review on Advanced Methodologies to Identify the Breast Cancer Classification using the Deep Learning Techniques

  • Bandaru, Satish Babu (Department of CSE, Annamalai University) ;
  • Babu, G. Rama Mohan (Department of CSE (AI&ML), RVR & JC college of Engineering)
  • Received : 2022.04.05
  • Published : 2022.04.30
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Abstract

Breast cancer is among the cancers that may be healed as the disease diagnosed at early times before it is distributed through all the areas of the body. The Automatic Analysis of Diagnostic Tests (AAT) is an automated assistance for physicians that can deliver reliable findings to analyze the critically endangered diseases. Deep learning, a family of machine learning methods, has grown at an astonishing pace in recent years. It is used to search and render diagnoses in fields from banking to medicine to machine learning. We attempt to create a deep learning algorithm that can reliably diagnose the breast cancer in the mammogram. We want the algorithm to identify it as cancer, or this image is not cancer, allowing use of a full testing dataset of either strong clinical annotations in training data or the cancer status only, in which a few images of either cancers or noncancer were annotated. Even with this technique, the photographs would be annotated with the condition; an optional portion of the annotated image will then act as the mark. The final stage of the suggested system doesn't need any based labels to be accessible during model training. Furthermore, the results of the review process suggest that deep learning approaches have surpassed the extent of the level of state-of-of-the-the-the-art in tumor identification, feature extraction, and classification. in these three ways, the paper explains why learning algorithms were applied: train the network from scratch, transplanting certain deep learning concepts and constraints into a network, and (another way) reducing the amount of parameters in the trained nets, are two functions that help expand the scope of the networks. Researchers in economically developing countries have applied deep learning imaging devices to cancer detection; on the other hand, cancer chances have gone through the roof in Africa. Convolutional Neural Network (CNN) is a sort of deep learning that can aid you with a variety of other activities, such as speech recognition, image recognition, and classification. To accomplish this goal in this article, we will use CNN to categorize and identify breast cancer photographs from the available databases from the US Centers for Disease Control and Prevention.

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References

  1. Afework, Y.K.; Debelee, T.G. Detection of Bacterial Wilt on Enset Crop Using Deep Learning Approach. Int. J. Eng. Res. Afr. 2020, 51, 1-17. https://doi.org/10.4028/www.scientific.net/JERA.51.1
  2. Shen, L.; Margolies, L.R.; Rothstein, J.H.; Fluder, E.; McBride, R.; Sieh, W. Deep Learning to Improve Breast Cancer Detection on Screening Mammography. Sci. Rep. 2019, 9.
  3. Suzuki, K. Survey of Deep Learning Applications to Medical Image Analysis. Med. Imaging Technol. 2017, 35, 212-226.
  4. Litjens, G.; Kooi, T.; Bejnordi, B.E.; Setio, A.A.A.; Ciompi, F.; Ghafoorian, M.; van der Laak, J.A.W.M.; van Ginneken, B.; Sanchez, C.I. A survey on deep learning in medical image analysis. Med. Image Anal. 2017, 42, 60-88. https://doi.org/10.1016/j.media.2017.07.005
  5. Shen, D.; Wu, G.; Suk, H.I. Deep Learning in Medical Image Analysis. Annu. Rev. Biomed. Eng. 2017, 19, 221-248. https://doi.org/10.1146/annurev-bioeng-071516-044442
  6. Suzuki, K. Overview of deep learning in medical imaging. Radiol. Phys. Technol. 2017, 10, 257-273. https://doi.org/10.1007/s12194-017-0406-5
  7. Lee, J.; Jun, S.; Cho, Y.; Lee, H.; Kim, G.B.; Seo, J.B.; Kim, N. Deep Learning in Medical Imaging: General Overview. Korean J. Radiol. 2017, 18, 570-584. https://doi.org/10.3348/kjr.2017.18.4.570
  8. Debelee, T.G.; Schwenker1, F.; Rahimeto, S.; Yohannes, D. Evaluation of modified adaptive k-means segmentation algorithm. Comput. Vis. Media 2019.
  9. Kebede, S.R.; Debelee, T.G.; Schwenker, F.; Yohannes, D. Classifier Based Breast Cancer Segmentation. J. Biomim. Biomater. Biomed. Eng. 2020, 47, 1-21.
  10. Rahimeto, S.; Debelee, T.; Yohannes, D.; Schwenker, F. Automatic pectoral muscle removal in mammograms. Evol. Syst. 2019.
  11. Debelee, T.G.; Schwenker, F.; Ibenthal, A.; Yohannes, D. Survey of deep learning in breast cancer image analysis. Evol. Syst. 2019.
  12. Debelee, T.G.; Gebreselasie, A.; Schwenker, F.; Amirian, M.; Yohannes, D. Classification of Mammograms Using Texture and CNN Based Extracted Features. J. Biomim. Biomater. Biomed. Eng. 2019, 42, 79-97.
  13. Debelee, T.G.; Amirian, M.; Ibenthal, A.; Palm, G.; Schwenker, F. Classification of Mammograms Using Convolutional Neural Network Based Feature Extraction. LNICST 2018, 244, 89-98.
  14. Suckling, J.; Parker, J.; Dance, D.; Astley, S.; Hutt, I.; Boggis, C.; Ricketts, I. Mammographic Image Analysis Society (MIAS) Database v1.21 [Dataset]; Dataset; Digital Mammogram Database Exerpta Medica: Dordrecht, The Netherland, 2015.
  15. Scuccimarra, E.A. DDSM Mammography [Dataset]; Dataset; Digital Mammogram Database Exerpta Medica: Dordrecht, The Netherland, 2018.
  16. Moreira, I.C.; Amaral, I.; Domingues, I.; Cardoso, A.; Cardoso, M.J.; Cardoso, J.S. INbreast. Acad. Radiol. 2012, 19, 236-248. https://doi.org/10.1016/j.acra.2011.09.014
  17. Dua, D.; Graff, C. UCI Machine Learning Repository; University of California, Irvine, School of Information and Computer Sciences: Newport Beach, CA, USA, 2017.
  18. Bukun. Breast Cancer Histopathological Database (BreakHis); Dataset; P and D Laboratory-Pathological Anatomy and Cytopathology: Parana, Brazil, 2019.
  19. Aresta, G.; Araujo, T.; Kwok, S.; Chennamsetty, S.S.; Safwan, M.; Alex, V.; Marami, B.; Prastawa, M.; Chan, M.; Donovan, M.; et al. BACH: Grand challenge on breast cancer histology images. Med. Image Anal. 2019, 56, 122-139. https://doi.org/10.1016/j.media.2019.05.010
  20. Wu, N.; Phang, J.; Park, J.; Shen, Y.; Huang, Z.; Zorin, M.; Jastrzebski, S.; Fevry, T.; Katsnelson, J.; Kim, E.; et al. Deep Neural Networks Improve Radiologists' Performance in Breast Cancer Screening. IEEE Trans. Med. Imaging 2020, 39, 1184-1194. https://doi.org/10.1109/tmi.2019.2945514
  21. Alzubaidi, L.; Al-Shamma, O.; Fadhel, M.A.; Farhan, L.; Zhang, J.; Duan, Y. Optimizing the Performance of Breast Cancer Classification by Employing the Same Domain Transfer Learning from Hybrid Deep Convolutional Neural Network Model. Electronics 2020, 9, 445. https://doi.org/10.3390/electronics9030445
  22. Zhu, Z.; Harowicz, M.; Zhang, J.; Saha, A.; Grimm, L.J.; Hwang, E.S.; Mazurowski, M.A. Deep learning analysis of breast MRIs for prediction of occult invasive disease in ductal carcinoma in situ. Comput. Biol. Med. 2019, 115, 103498. https://doi.org/10.1016/j.compbiomed.2019.103498
  23. Li, X.; Qin, G.; He, Q.; Sun, L.; Zeng, H.; He, Z.; Chen, W.; Zhen, X.; Zhou, L. Digital breast tomosynthesis versus digital mammography: Integration of image modalities enhances deep learning-based breast mass classification. Eur. Radiol. 2019, 30, 778-788. https://doi.org/10.1007/s00330-019-06457-5
  24. Zeiser, F.A.; da Costa, C.A.; Zonta, T.; Marques, N.M.C.; Roehe, A.V.; Moreno, M.; da Rosa Righi, R. Segmentation of Masses on Mammograms Using Data Augmentation and Deep Learning. J. Digit. Imaging 2020.
  25. Zhang, Y.; Chen, J.H.; Chang, K.T.; Park, V.Y.; Kim, M.J.; Chan, S.; Chang, P.; Chow, D.; Luk, A.; Kwong, T.; et al. Automatic Breast and Fibroglandular Tissue Segmentation in Breast MRI Using Deep Learning by a Fully-Convolutional Residual Neural Network U-Net. Acad. Radiol. 2019, 26, 1526-1535. https://doi.org/10.1016/j.acra.2019.01.012
  26. Zhou, J.; Luo, L.Y.; Dou, Q.; Chen, H.; Chen, C.; Li, G.J.; Jiang, Z.F.; Heng, P.A. Weakly supervised 3D deep learning for breast cancer classification and localization of the lesions in MR images. J. Magn. Reson. Imaging 2019, 50, 1144-1151. https://doi.org/10.1002/jmri.26721
  27. Huang, G.; Liu, Z.; Maaten, L.V.D.; Weinberger, K.Q. Densely Connected Convolutional Networks. In Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA, 21-26 July 2017.
  28. Zhang, J.; Saha, A.; Soher, B.J.; Mazurowski, M.A. Automatic deep learning-based normalization of breast dynamic contrast-enhanced magnetic resonance images. arXiv 2018, arXiv:1807.02152v1.
  29. Sheikh, T.S.; Lee, Y.; Cho, M. Histopathological Classification of Breast Cancer Images Using a Multi-Scale Input and Multi-Feature Network. Cancers 2020, 12, 2031. https://doi.org/10.3390/cancers12082031
  30. Li, X.; Shen, X.; Zhou, Y.; Wang, X.; Li, T.Q. Classification of breast cancer histopathological images using interleaved DenseNet with SENet (IDSNet). PLoS ONE 2020, 15, e0232127. https://doi.org/10.1371/journal.pone.0232127
  31. Yan, R.; Ren, F.; Wang, Z.; Wang, L.; Zhang, T.; Liu, Y.; Rao, X.; Zheng, C.; Zhang, F. Breast cancer histopathological image classification using a hybrid deep neural network. Methods 2020, 173, 52-60. https://doi.org/10.1016/j.ymeth.2019.06.014
  32. Sharma, S.; Mehra, R. Conventional Machine Learning and Deep Learning Approach for Multi-Classification of Breast Cancer Histopathology Images-a Comparative Insight. J. Digit. Imaging 2020, 33, 632-654. https://doi.org/10.1007/s10278-019-00307-y
  33. Vang, Y.S.; Chen, Z.; Xie, X. Deep Learning Framework for Multiclass Breast Cancer Histology Image Classification. In Lecture Notes in Computer Science; Springer International Publishing: Berlin/Heidelberg, Germany, 2018; pp. 914-922.
  34. Dembrower, K.; Liu, Y.; Azizpour, H.; Eklund, M.; Smith, K.; Lindholm, P.; Strand, F. Comparison of a Deep Learning Risk Score and Standard Mammographic Density Score for Breast Cancer Risk Prediction. Radiology 2020, 294, 265-272. https://doi.org/10.1148/radiol.2019190872
  35. Hagos, Y.B.; Merida, A.G.; Teuwen, J. Improving Breast Cancer Detection using Symmetry Information with Deep Learning. arXiv 2018, arXiv:1808.08273v1.