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A Deep Learning Architecture for Identification of Breast Cancer on Mammography by Learning Various Representations of Cancerous Mass

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Deep Learning for Cancer Diagnosis

Part of the book series: Studies in Computational Intelligence ((SCI,volume 908))

Abstract

Deep Learning (DL) is a high capable machine learning algorithm with the detailed analysis abilities on images. Although DL models achieve very high classification performances, the applications are trending on using and fine-tuning pre-trained DL models by transfer learning due to the dependence on the number of data, long train time, employments in modeling the most meaningful architecture. In this chapter, we proposed own pruned and simple DL architectures on ROIs extracted from mammography to classify cancer-normal using Convolutional Neural Network (CNN) and Deep Autoencoder (Deep AE) models, which are the most popular DL algorithms. Breast Cancer, which occurs as a result of developing of normal breast tissue to a tumour, is one of the deadliest diseases according to WHO reports. The detection of cancerous mass at early stages is one of the decisive step to start the treatment process. Mammography images are the most effective and simplest way of the diagnosis of breast cancer. Whereas early diagnosis of breast cancer is a hard process due to characteristics of mammography, the computer-assisted diagnosis systems have ability to perform a detailed analysis for a complete assessment. The aim of this study is proposing a robust cancer diagnosis model with a light-weighted DL architecture and comparing the efficiency of the dense layer with the Deep AE kernels against CNN. The ROIs from mammography images were fed into the DL algorithms and the achievements were evaluated. The proposed Deep AE architecture reached the classification performance rates of 95.17%, 96.81%, 93.65%, 93.38%, 96.95%, and 0.972 for overall accuracy, sensitivity, specificity, precision, NPV, and AUROC, respectively.

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References

  1. P.C. Gøtzsche, Screening for breast cancer with mammography. Lancet 358, 2167–2168 (2001). https://doi.org/10.1016/S0140-6736(01)07198-7

    Article  Google Scholar 

  2. A. Jalalian, S.B.T. Mashohor, H.R. Mahmud, M.I.B. Saripan, A.R.B. Ramli, B. Karasfi, Computer-aided detection/diagnosis of breast cancer in mammography and ultrasound: a review. Clin. Imaging 37(3), 420–426 (2013). https://doi.org/10.1016/j.clinimag.2012.09.024

    Article  Google Scholar 

  3. Q. Zeng, H. Jiang, L. Ma, Learning multi-level features for breast mass detection, in ACM International Conference Proceeding Series (2018). https://doi.org/10.1145/3285996.3286000

  4. H.P. Chan, R.K. Samala, L.M. Hadjiiski, C. Zhou, Deep learning in medical image analysis. Adv. Exp. Med. Biol. (2020). https://doi.org/10.1007/978-3-030-33128-3

  5. J. Shiraishi, Q. Li, D. Appelbaum, K. Doi, Computer-aided diagnosis and artificial intelligence in clinical imaging. Semin. Nucl. Med. (2011). https://doi.org/10.1053/j.semnuclmed.2011.06.004

  6. D. Abdelhafiz, C. Yang, R. Ammar, S. Nabavi, Deep convolutional neural networks for mammography: Advances, challenges and applications. BMC Bioinformatics (2019). https://doi.org/10.1186/s12859-019-2823-4

  7. G. Altan, Y. Kutlu, Generative Autoencoder Kernels on Deep Learning for Brain Activity Analysis. Natural and Engineering Sciences (2018). https://doi.org/10.28978/nesciences.468978

  8. K. Mendel, H. Li, D. Sheth, M. Giger, Transfer learning from convolutional neural networks for computer-aided diagnosis: a comparison of digital breast tomosynthesis and full-field digital mammography. Acad. Radiol. 26(6), 735–743 (2019). https://doi.org/10.1016/j.acra.2018.06.019

    Article  Google Scholar 

  9. M.A. Al-antari, M.A. Al-masni, S.U. Park, J.H. Park, M.K. Metwally, Y.M. Kadah, S.M. Han, T.S. Kim, An automatic computer-aided diagnosis system for breast cancer in digital mammograms via deep belief network. J. Med. Biol. Eng. (2018). https://doi.org/10.1007/s40846-017-0321-6

  10. G. Altan, Y. Kutlu, A.Ö. Pekmezci, A. Yayık, Diagnosis of chronic obstructive pulmonary disease using deep extreme learning machines with LU autoencoder kernel, in 7th International Conference on Advanced Technologies (ICAT’18) (2018)

    Google Scholar 

  11. S.J.A. Sarosa, F. Utaminingrum, F.A. Bachtiar, Mammogram breast cancer classification using gray-level co-occurrence matrix and support vector machine, in 3rd International Conference on Sustainable Information Engineering and Technology, SIET 2018—Proceedings (2018). https://doi.org/10.1109/SIET.2018.8693146

  12. R. Wang, Y. Ma, W. Sun, Y. Guo, W. Wang, Y. Qi, X. Gong, Multi-level nested pyramid network for mass segmentation in mammograms. Neurocomputing 363, 313–320 (2019). https://doi.org/10.1016/j.neucom.2019.06.045

    Article  Google Scholar 

  13. W. Zhu, X. Xiang, T.D. Tran, G.D. Hager, X. Xie, Adversarial deep structured nets for mass segmentation from mammograms, in 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018) (2018), pp. 847–850. https://doi.org/10.1109/ISBI.2018.8363704

  14. G. Altan, Y. Kutlu, Hessenberg Elm autoencoder kernel for deep learning. J. Eng. Technol. Appl. Sci. 3(2), 141–151 (2018). https://doi.org/10.30931/jetas.450252

    Article  Google Scholar 

  15. J. Arevalo, A. Cruz-Roa, F.A. González, Hybrid Image Representation Learning Model with Invariant Features for Basal Cell Carcinoma Detection, ed. by J. Brieva, B. Escalante-Ramírez (2013), pp. 89220M-6). https://doi.org/10.1117/12.2035530

  16. J. Arevalo, F.A. Gonzalez, R. Ramos-Pollan, J.L. Oliveira, M.A.G. Lopez et al., DeepMammo breast mass classification using deep convolutional neural networks. Comput. Methods Progr. Biomed. (2018). https://doi.org/10.1016/j.acra.2018.06.019

  17. S. Pan, J. Zhang, T. Wang, L. Kong, X-ray mammary image segmentation based on convolutional neural network, in 2019 IEEE 4th International Conference on Image, Vision and Computing (ICIVC) (2019), pp. 105–108. https://doi.org/10.1109/ICIVC47709.2019.8981350

  18. S. Yoon, S. Kim, AdaBoost-based multiple SVM-RFE for classification of mammograms in DDSM. BMC Med. Inform. Decis. Mak. (2009). https://doi.org/10.1186/1472-6947-9-S1-S1

    Article  Google Scholar 

  19. P. Xi, C. Shu, R. Goubran, Abnormality detection in mammography using deep convolutional neural networks, in IEEE International Symposium on Medical Measurements and Applications (MeMeA) (2018), pp. 1–6. https://doi.org/10.1109/MeMeA.2018.8438639

  20. H. Nasir Khan, A.R. Shahid, B. Raza, A.H. Dar, H. Alquhayz, Multi-view feature fusion based four views model for mammogram classification using convolutional neural network. IEEE Access (2019). https://doi.org/10.1109/ACCESS.2019.2953318

  21. B. Pardamean, T.W. Cenggoro, R. Rahutomo, A. Budiarto, E.K. Karuppiah, Transfer learning from chest X-ray pre-trained convolutional neural network for learning mammogram data. Procedia Comput. Sci. 135, 400–407 (2018). https://doi.org/10.1016/j.procs.2018.08.190

    Article  Google Scholar 

  22. B. Swiderski, J. Kurek, S. Osowski, M. Kruk, W. Barhoumi, Deep learning and non-negative matrix factorization in recognition of mammograms, in Eighth International Conference on Graphic and Image Processing (ICGIP 2016) (2017). https://doi.org/10.1117/12.2266335

  23. R. Agarwal, O. Diaz, R. Marti, X. Llado, Mass detection in mammograms using pre-trained deep learning models, in 14th International Workshop on Breast Imaging (IWBI 2018), ed. by E.A. Krupinski (SPIE, 2018), p. 12. https://doi.org/10.1117/12.2317681

  24. V.D. Nguyen, K. Lim, M.D. Le, N. Dung Bui, Combination of Gabor filter and convolutional neural network for suspicious mass classification, in 2018 22nd International Computer Science and Engineering Conference (ICSEC) (2018), pp. 1–4. https://doi.org/10.1109/ICSEC.2018.8712796

  25. M.G. Ertosun, D.L. Rubin, Probabilistic visual search for masses within mammography images using deep learning, in 2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (2015), pp. 1310–1315. https://doi.org/10.1109/BIBM.2015.7359868

  26. S. Suzuki, X. Zhang, N. Homma, K. Ichiji, N. Sugita, Y. Kawasumi, T. Ishibashi, M. Yoshizawa, Mass detection using deep convolutional neural network for mammographic computer-aided diagnosis, in 2016 55th Annual Conference of the Society of Instrument and Control Engineers of Japan (SICE) (2016), pp. 1382–1386. https://doi.org/10.1109/SICE.2016.7749265

  27. R. Touahri, N. AzizI, N.E. Hammami, M. Aldwairi, F. Benaida, Automated breast tumor diagnosis using local binary patterns (LBP) based on deep learning classification, in International Conference on Computer and Information Sciences (ICCIS) (2019), pp. 1–5. https://doi.org/10.1109/ICCISci.2019.8716428

  28. P. Vincent, H. Larochelle, Y. Bengio, P.-A. Manzagol, Extracting and composing robust features with denoising autoencoders (2008). https://doi.org/10.1145/1390156.1390294

  29. J. Wang, X. Zhang, Q. Gao, H. Yue, H. Wang, Device-free wireless localization and activity recognition: a deep learning approach. IEEE Trans. Vehic. Technol. 66(7), 6258–6267 (2017). https://doi.org/10.1109/TVT.2016.2635161

    Article  Google Scholar 

  30. B.A. Olshausen, D.J. Field, Sparse coding with an overcomplete basis set: a strategy employed by V1? Vis. Res. (1997). https://doi.org/10.1016/S0042-6989(97)00169-7

  31. M. Heath, K. Bowyer, D. Kopans, R. Moore, W. Philip Kegelmeyer, The digital database for screening mammography, in The Fifth International Workshop on Digital Mammography, ed. by M.J. Yaffe (Medical Physics Publishing, 2001). ISBN 1-930524-00-5

    Google Scholar 

  32. R. Duda, P. Hart, D. Stork, Patterns Classification (Wiley, New York, 2012). ISBN: 9781118586006

    Google Scholar 

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Authors and Affiliations

  1. Iskenderun Technical University, Computer Engineering Department, Hatay, Turkey

    Gokhan Altan

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  1. Gokhan Altan
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Correspondence to Gokhan Altan .

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Editors and Affiliations

  1. Department of Computer Engineering, Süleyman Demirel University, Isparta, Turkey

    Utku Kose

  2. Faculty of Engineering, Al-Balqa Applied University, Balqa, Jordan

    Jafar Alzubi

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Altan, G. (2021). A Deep Learning Architecture for Identification of Breast Cancer on Mammography by Learning Various Representations of Cancerous Mass. In: Kose, U., Alzubi, J. (eds) Deep Learning for Cancer Diagnosis. Studies in Computational Intelligence, vol 908. Springer, Singapore. https://doi.org/10.1007/978-981-15-6321-8_10

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