Research Article
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Year 2023, , 57 - 65, 22.03.2023
https://doi.org/10.17798/bitlisfen.1190134

Abstract

Supporting Institution

Siirt Üniversitesi Bilimsel Araştırma Projeleri Koordinatörlüğü

Project Number

2021-SİÜMÜH-01

Thanks

Yazar Siirt Üniversitesi Bilimsel Araştırma Projeleri Koordinatörlüğüne bu çalışmanın 2021-SİÜMÜH-01 proje kapsamında desteklenmesinden dolayı teşekkür eder.

References

  • [1] H. L. Bleich, “The computer as a consultant,” N. Engl. J. Med., vol. 284, no. 3, pp. 141–7, 1971.
  • [2] M. E. Cohen and D. Le Hudson, “A hybrid system for diagnosis involving biosignals,” Proc. 2005 IEEE Int. Conf. Comput. Intell. Meas. Syst. Appl. CIMSA 2005, vol. 2005, no. July, pp. 312–315, 2005.
  • [3] I. Gökbay, S. Karaman, S. Yarman, and B. Yarman, “An intelligent decision support tool for early diagnosis of functional pituitary adenomas,” TWMS J. Appl. Eng. Math., vol. 5, no. 2, p. 169, 2015.
  • [4] P. Xi, C. Shu, and R. Goubran, “Abnormality Detection in Mammography using Deep Convolutional Neural Networks,” in 2018 IEEE International Symposium on Medical Measurements and Applications, Proceedings, 2018, pp. 1–6.
  • [5] J. Tang, R. M. Rangayyan, J. Xu, I. E. El Naqa, and Y. Yang, “Computer-aided detection and diagnosis of breast cancer with mammography: Recent advances,” IEEE Trans. Inf. Technol. Biomed., vol. 13, no. 2, pp. 236–251, 2009.
  • [6] V. Ozmen, “Breast Cancer in Turkey: Clinical and Histopathological Characteristics (Analysis of 13.240 Patients),” J. Breast Heal., vol. 10, no. 2, pp. 98–105, 2014.
  • [7] L. Ackerman and E. Gose, “Breast lesion classification by computer and xeroradiograph,” Cancer, vol. 30, no. 4, pp. 1025–1035, 1972.
  • [8] Y. Kaya, “A new intelligent classifier for breast cancer diagnosis based on a rough set and extreme learning machine: Rs + elm,” Turkish J. Electr. Eng. Comput. Sci., vol. 21, no. SUPPL. 1, pp. 2079–2091, 2013.
  • [9] P. Görgel, A. Sertbas, and O. N. Uçan, “Computer-aided classification of breast masses in mammogram images based on spherical wavelet transform and support vector machines,” Expert Syst., vol. 32, no. 1, pp. 155–164, 2015.
  • [10] A. Jain and D. Levy, “Breast Mass Classification from Mammograms using Deep Convolutional Neural Networks,” arXiv Prepr. arXiv1612.00542, 2016.
  • [11] N. Wu et al., “Deep Neural Networks Improve Radiologists’ Performance in Breast Cancer Screening,” IEEE Trans. Med. Imaging, vol. 39, no. 4, pp. 1184–1194, 2020.
  • [12] L. Shen, L. R. Margoiles, J. H. Rothstein, E. Fluder, R. McBride, and W. Sieh, “Deep Learning to improve Breast cancer Detection on Screening Mammography,” Sci. Rep., vol. 9, no. 12495, pp. 1–12, 2019.
  • [13] B. Zoph, V. Vasudevan, J. Shlens, and Q. V. Le, “Learning Transferable Architectures for Scalable Image Recognition,” Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., pp. 8697–8710, 2018.
  • [14] Jia Deng, Wei Dong, R. Socher, Li-Jia Li, Kai Li, and Li Fei-Fei, “ImageNet: A large-scale hierarchical image database,” in 2009 IEEE Conference on Computer Vision and Pattern Recognition, 2009, pp. 248–255.
  • [15] M. Tan and Q. V. Le, “EfficientNet: Rethinking model scaling for convolutional neural networks,” 36th Int. Conf. Mach. Learn. ICML 2019, vol. 2019-June, pp. 10691–10700, 2019.
  • [16] K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2016, vol. 2016-Decem, pp. 770–778.
  • [17] F. Chollet, “Xception: Deep learning with depthwise separable convolutions,” Proc. - 30th IEEE Conf. Comput. Vis. Pattern Recognition, CVPR 2017, vol. 2017-Janua, pp. 1800–1807, 2017.
  • [18] R. S. Lee, F. Gimenez, A. Hoogi, and D. Rubin, “Curated Breast Imaging Subset of DDSM [Dataset],” 2016. [Online]. Available: https://wiki.cancerimagingarchive.net/display/Public/CBIS-DDSM#22516629cf2ec23796854d91bc86c4ae2e499baa.
  • [19] R. S. Lee, F. Gimenez, A. Hoogi, K. K. Miyake, M. Gorovoy, and D. L. Rubin, “A curated mammography data set for use in computer-aided detection and diagnosis research,” Sci. Data, vol. 4, pp. 1–9, 2017.
  • [20] K. Clark et al., “The Cancer Imaging Archive (TCIA): maintaining and operating a public information repository.,” J. Digit. Imaging, vol. 26, no. 6, pp. 1045–1057, Dec. 2013.
  • [21] M. Heath et al., “Current status of the Digital Database for Screening Mammography,” in Proceedings of the Fourth International Workshop on Digital Mammography, 1998, pp. 457–460.
  • [22] M. Heath, K. Bowyer, D. Kopans, R. Moore, and W. P. Kegelmeyer, “The Digital Database for Screening Mammography,” in Proceedings of the Fifth International Workshop on Digital Mammography, 2001, pp. 212–218.
  • [23] O. Russakovsky et al., “ImageNet Large Scale Visual Recognition Challenge,” Int. J. Comput. Vis., vol. 115, no. 3, pp. 211–252, 2015.
  • [24] D. P. Kingma and J. L. Ba, “Adam: A method for stochastic optimization,” 3rd Int. Conf. Learn. Represent. ICLR 2015 - Conf. Track Proc., pp. 1–15, 2015.
  • [25] F. Pedregosa et al., “Scikit-learn: Machine Learning in Python,” J. Mach. Learn. Res., vol. 12, pp. 2825–2830, 2011.

Deep Transfer Learning to Classify Mass and Calcification Pathologies from Screen Film Mammograms

Year 2023, , 57 - 65, 22.03.2023
https://doi.org/10.17798/bitlisfen.1190134

Abstract

The number of breast cancer diagnosis is the biggest among all cancers, but it can be treated if diagnosed early. Mammography is commonly used for detecting abnormalities and diagnosing the breast cancer. Breast cancer screening and diagnosis are still being performed by radiologists. In the last decade, deep learning was successfully applied on big image classification databases such as ImageNet. Deep learning methods for the automated breast cancer diagnosis is under investigation. In this study, breast cancer mass and calcification pathologies are classified by using deep transfer learning methods. A total of 3,360 patches were used from the Digital Database for Screening Mammography (DDSM) and CBIS-DDSM mammogram databases for convolutional neural network training and testing. Transfer learning was applied using Resnet50, Xception, NASNet, and EfficientNet-B7 network backbones. The best classification performance was achieved by the Xception network. On the original CBIS-DDSM test data, an AUC of 0.9317 was obtained for the five-way classification problem. The results are promising for the implementation of automated diagnosis of breast cancer.

Project Number

2021-SİÜMÜH-01

References

  • [1] H. L. Bleich, “The computer as a consultant,” N. Engl. J. Med., vol. 284, no. 3, pp. 141–7, 1971.
  • [2] M. E. Cohen and D. Le Hudson, “A hybrid system for diagnosis involving biosignals,” Proc. 2005 IEEE Int. Conf. Comput. Intell. Meas. Syst. Appl. CIMSA 2005, vol. 2005, no. July, pp. 312–315, 2005.
  • [3] I. Gökbay, S. Karaman, S. Yarman, and B. Yarman, “An intelligent decision support tool for early diagnosis of functional pituitary adenomas,” TWMS J. Appl. Eng. Math., vol. 5, no. 2, p. 169, 2015.
  • [4] P. Xi, C. Shu, and R. Goubran, “Abnormality Detection in Mammography using Deep Convolutional Neural Networks,” in 2018 IEEE International Symposium on Medical Measurements and Applications, Proceedings, 2018, pp. 1–6.
  • [5] J. Tang, R. M. Rangayyan, J. Xu, I. E. El Naqa, and Y. Yang, “Computer-aided detection and diagnosis of breast cancer with mammography: Recent advances,” IEEE Trans. Inf. Technol. Biomed., vol. 13, no. 2, pp. 236–251, 2009.
  • [6] V. Ozmen, “Breast Cancer in Turkey: Clinical and Histopathological Characteristics (Analysis of 13.240 Patients),” J. Breast Heal., vol. 10, no. 2, pp. 98–105, 2014.
  • [7] L. Ackerman and E. Gose, “Breast lesion classification by computer and xeroradiograph,” Cancer, vol. 30, no. 4, pp. 1025–1035, 1972.
  • [8] Y. Kaya, “A new intelligent classifier for breast cancer diagnosis based on a rough set and extreme learning machine: Rs + elm,” Turkish J. Electr. Eng. Comput. Sci., vol. 21, no. SUPPL. 1, pp. 2079–2091, 2013.
  • [9] P. Görgel, A. Sertbas, and O. N. Uçan, “Computer-aided classification of breast masses in mammogram images based on spherical wavelet transform and support vector machines,” Expert Syst., vol. 32, no. 1, pp. 155–164, 2015.
  • [10] A. Jain and D. Levy, “Breast Mass Classification from Mammograms using Deep Convolutional Neural Networks,” arXiv Prepr. arXiv1612.00542, 2016.
  • [11] N. Wu et al., “Deep Neural Networks Improve Radiologists’ Performance in Breast Cancer Screening,” IEEE Trans. Med. Imaging, vol. 39, no. 4, pp. 1184–1194, 2020.
  • [12] L. Shen, L. R. Margoiles, J. H. Rothstein, E. Fluder, R. McBride, and W. Sieh, “Deep Learning to improve Breast cancer Detection on Screening Mammography,” Sci. Rep., vol. 9, no. 12495, pp. 1–12, 2019.
  • [13] B. Zoph, V. Vasudevan, J. Shlens, and Q. V. Le, “Learning Transferable Architectures for Scalable Image Recognition,” Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., pp. 8697–8710, 2018.
  • [14] Jia Deng, Wei Dong, R. Socher, Li-Jia Li, Kai Li, and Li Fei-Fei, “ImageNet: A large-scale hierarchical image database,” in 2009 IEEE Conference on Computer Vision and Pattern Recognition, 2009, pp. 248–255.
  • [15] M. Tan and Q. V. Le, “EfficientNet: Rethinking model scaling for convolutional neural networks,” 36th Int. Conf. Mach. Learn. ICML 2019, vol. 2019-June, pp. 10691–10700, 2019.
  • [16] K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2016, vol. 2016-Decem, pp. 770–778.
  • [17] F. Chollet, “Xception: Deep learning with depthwise separable convolutions,” Proc. - 30th IEEE Conf. Comput. Vis. Pattern Recognition, CVPR 2017, vol. 2017-Janua, pp. 1800–1807, 2017.
  • [18] R. S. Lee, F. Gimenez, A. Hoogi, and D. Rubin, “Curated Breast Imaging Subset of DDSM [Dataset],” 2016. [Online]. Available: https://wiki.cancerimagingarchive.net/display/Public/CBIS-DDSM#22516629cf2ec23796854d91bc86c4ae2e499baa.
  • [19] R. S. Lee, F. Gimenez, A. Hoogi, K. K. Miyake, M. Gorovoy, and D. L. Rubin, “A curated mammography data set for use in computer-aided detection and diagnosis research,” Sci. Data, vol. 4, pp. 1–9, 2017.
  • [20] K. Clark et al., “The Cancer Imaging Archive (TCIA): maintaining and operating a public information repository.,” J. Digit. Imaging, vol. 26, no. 6, pp. 1045–1057, Dec. 2013.
  • [21] M. Heath et al., “Current status of the Digital Database for Screening Mammography,” in Proceedings of the Fourth International Workshop on Digital Mammography, 1998, pp. 457–460.
  • [22] M. Heath, K. Bowyer, D. Kopans, R. Moore, and W. P. Kegelmeyer, “The Digital Database for Screening Mammography,” in Proceedings of the Fifth International Workshop on Digital Mammography, 2001, pp. 212–218.
  • [23] O. Russakovsky et al., “ImageNet Large Scale Visual Recognition Challenge,” Int. J. Comput. Vis., vol. 115, no. 3, pp. 211–252, 2015.
  • [24] D. P. Kingma and J. L. Ba, “Adam: A method for stochastic optimization,” 3rd Int. Conf. Learn. Represent. ICLR 2015 - Conf. Track Proc., pp. 1–15, 2015.
  • [25] F. Pedregosa et al., “Scikit-learn: Machine Learning in Python,” J. Mach. Learn. Res., vol. 12, pp. 2825–2830, 2011.
There are 25 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Araştırma Makalesi
Authors

Volkan Müjdat Tiryaki 0000-0003-1824-5260

Project Number 2021-SİÜMÜH-01
Publication Date March 22, 2023
Submission Date October 17, 2022
Acceptance Date March 1, 2023
Published in Issue Year 2023

Cite

IEEE V. M. Tiryaki, “Deep Transfer Learning to Classify Mass and Calcification Pathologies from Screen Film Mammograms”, Bitlis Eren Üniversitesi Fen Bilimleri Dergisi, vol. 12, no. 1, pp. 57–65, 2023, doi: 10.17798/bitlisfen.1190134.



Bitlis Eren Üniversitesi
Fen Bilimleri Dergisi Editörlüğü

Bitlis Eren Üniversitesi Lisansüstü Eğitim Enstitüsü        
Beş Minare Mah. Ahmet Eren Bulvarı, Merkez Kampüs, 13000 BİTLİS        
E-posta: fbe@beu.edu.tr