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In this study, for the diagnosis and classification of breast cancer, we used and applied five classical pre-trained deep convolutional neural network models (DCNN) which have proven successful many times in different fields (ResNet-18, AlexNet, GoogleNet and SuffleNet). To make pre-trained DCNN models suitable for the purpose of our study, we updated some layers according to the new situation by using the transfer learning technique. We did not change the weights of all layers used in these five pre-trained DCNN models. Instead, we just gave new weights to the new layers so that new layers adapt faster to emerging new DCNN models. With these five pre-trained DCNN models, we have realized a quadruple classification as "cancer", "normal", "actionable" and "benign", and a binary classification as "actionable + cancer" and "normal + benign". With these two separate classification and diagnosis studies, we have carried out comparative experimental examination and analysis of pre-trained DCNN models for breast cancer diagnosis. In the study, it was concluded that successful results can be achieved with pre-trained DCNN models without extra time-consuming procedures such as feature extraction, and DCNN can perform quite successfully in cancer diagnosis and image comment.
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1--8
Opis fizyczny
Bibliogr. 31 poz., fig., tab.
Twórcy
autor
- Department of Electrical and Energy, Kayseri University, Kayseri, Turkey
- Department of Electrical Engineering, Istanbul Technical University, Istanbul, Turkey
autor
- Department of Electrical and Electronics Eng., Batman University, Batman, Turkey
Bibliografia
- 1. Abhinav G. Deep Learning Reading Group: SqueezeNet. KDnuggets; 2018.
- 2. Buda M., Saha A., Walsh R., Ghate S., Li N., Święcicki A., Lo J.Y., Yang J., Mazurowski M.A. Data from the Breast Cancer Screening – Digital Breast Tomosynthesis (BCS-DBT). Data from The Cancer Imaging Archive; 2020. DOI: 10.7937/e4wt-cd02.
- 3. Clark K., Vendt B., Smith K., Freymann J., Kirby J., Koppel P., Moore S., Phillips S., Maffitt D., Pringle M., Tarbox L., Prior F. The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository, Journal of Digital Imaging. 2013;26(6):1045–1057. DOI: 10.1007/ s10278-013-9622-7
- 4. Agnes S.A., Anitha J., Pandian S.I.A., Peter J.D. Classification of Mammogram Images Using Multiscale all Convolutional Neural Network (MACNN). Journal of Medical Systems. 2020;44(1):1– 9. DOI: ARTN 30 10.1007/s10916-019-1494-z
- 5. Berbar M.A. Hybrid methods for feature extraction for breast masses classification. Egyptian Informatics Journal. 2018;19(1):63–73. DOI:10.1016/j. eij.2017.08.001
- 6. Bharti D., Sahoo G.R., Shukla S., Pradhan A. Wavelet transform of Fourier domain low coherence interference spectra for breast cancer detection. Paper presented at the Tissue Optics and Photonics; 2020.
- 7. Binder A., Bockmayr M., Hagele M., Wienert S., Heim D., Hellweg K., Klauschen F. Morphological and molecular breast cancer profiling through explainable machine learning. Nature Machine Intelligence. 2021;3(4), 355–366. DOI:10.1038/ s42256-021-00303-4
- 8. Bishop C.M. Pattern Recognition and Machine Learning (Information Science and Statistics) (Softcover reprint of the original 1st ed. 2006 ed.). Springer. 2016.
- 9. Cuzick J., Sestak I., Forbes J.F., Dowsett M., Cawthorn S., Mansel R.E., Howell A.J.T.L. Use of anastrozole for breast cancer prevention (IBIS-II): long-term results of a randomised controlled trial. 2020;395(10218):117–122.
- 10. De Santana, M.A., Pereira, J.M.S., da Silva W.W.A., dos Santos W.P. 2021. Breast Cancer Diagnosis in Mammograms Using Wavelet Analysis, Haralick Descriptors, and Autoencoder. In AI Innovation in Medical Imaging Diagnostics. IGI Global. 2021;76-91.
- 11. Desai M., Shah M.J.C.E. An anatomization on Breast Cancer Detection and Diagnosis employing Multi-layer Perceptron Neural Network (MLP) and Convolutional Neural Network (CNN); 2020.
- 12. Gupta K.K., Vijay R., Pahadiya P.J.S.C.T., Applications. A Review Paper on Feature Selection Techniques and Artificial Neural Networks Architectures Used in Thermography for Early Stage Detection of Breast Cancer. 2020;455–465.
- 13. Hakkoum H., Idri A., Abnane I. Assessing and comparing interpretability techniques for artificial neural networks breast cancer classication. Computer Methods in Biomechanics and Biomedical Engineering-Imaging and Visualization. 2021;1– 13. DOI:10.1080/21681163.2021.1901784
- 14. He K, Zhang X., Ren S., Sun J. Deep Residual Learning for Image Recognition. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, 2016, 770–778, DOI: 10.1109/CVPR.2016.90.
- 15. Hou C., Zhong X., He P., Xu B., Diao S., Yi F., Li J.J.J.M.I. Predicting breast cancer in Chinese women using machine learning techniques: algorithm development. 2020;8(6):e17364.
- 16. Kowal M., Filipczuk P., Obuchowicz A., Korbicz J., Monczak R. Computer-aided diagnosis of breast cancer based on fine needle biopsy microscopic images. Comput Biol Med. 2013;43(10):1563–1572. DOI: 10.1016/j.compbiomed.2013.08.003
- 17. Krizhevsky A., Sutskever I., Hinton G.E. ImageNet classification with deep convolutional neural networks. Communications of the ACM. 2017;.60(6):84–90. DOI:10.1145/3065386
- 18. Lei Y., He X., Yao J., Wang T., Wang L., Li W., Yang X.J.M.P. Breast tumor segmentation in 3D automatic breast ultrasound using Mask scoring R‐ CNN. 2021;48(1):204–214.
- 19. Liu J., Cheng H., Lv X.Y., Zhang Z.X., Zheng X.X., Wu G.H., Yue X.X. Use of FT-IR spectroscopy combined with SVM as a screening tool to identify invasive ductal carcinoma in breast cancer. Optik. 2020;204:164225. DOI: ARTN 164225.10.1016/j. ijleo.2020.164225
- 20. Michelucci U. Advanced Applied Deep Learning: Convolutional Neural Networks and Object Detection (1st ed.). Apress; 2019.
- 21. Oh C. M., Lee D., Kong H.J., Lee S., Won Y.J., Jung K. W., Cho H. Causes of death among cancer patients in the era of cancer survivorship in Korea: Attention to the suicide and cardiovascular mortality. Cancer Medicine. 2020;9(5):1741–1752. DOI:10.1002/cam4.2813
- 22. Patnaik J.L., Byers T., DiGuiseppi C., Dabelea D., Denberg T.D.J.B.C.R. Cardiovascular disease competes with breast cancer as the leading cause of death for older females diagnosed with breast cancer: a retrospective cohort study. 2011;13(3):1–9.
- 23. Rasheed A., Younis M.S., Qadir J., Bilal M.J. a.p.a. Use of Transfer Learning and Wavelet Transform for Breast Cancer Detection; 2021.
- 24. Rawal G., Rawal R., Shah H., Patel K.. A Comparative Study Between Artificial Neural Networks and Conventional Classifiers for Predicting Diagnosis of Breast Cancer. In ICDSMLA 2019. Springer. 2020; 261–271.
- 25. Singh S. & Kumar R. 2020. Histopathological image analysis for breast cancer detection using cubic SVM. Paper presented at the 2020 7th International Conference on Signal Processing and Integrated Networks (SPIN).
- 26. Szegedy C., Liu W., Jia Y., Sermanet P., Reed S., Anguelov D., Erhan D., Vanhoucke V., Rabinovich A. Going deeper with convolutions. IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Boston MA USA 2015 June.
- 27. Vaka A.R., Soni B., Reddy S.J.I.E. Breast cancer detection by leveraging Machine Learning. 2020;6(4):320–324.
- 28. Vrigazova B.P. Detection of Malignant and Benign Breast Cancer Using the ANOVA-BOOTSTRAPSVM. Journal of Data and Information Science. 2020;5(2):62–75. DOI:10.2478/jdis-2020-0012
- 29. Yousefi H., Maheronnaghsh M., Molaei F., Mashouri L., Reza Aref A., Momeny M., Alahari S.K. Long noncoding RNAs and exosomal lncRNAs: classification, and mechanisms in breast cancer metastasis and drug resistance. Oncogene. 2020;39(5):953– 974. DOI: 10.1038/s41388-019-1040-y
- 30. Zhang M., Li,Q.J. a. p. a.. MS-GWNN: multiscale graph wavelet neural network for breast cancer diagnosis; 2020.
- 31. Zhang X., Zhou X., Lin M., Sun J. ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018, 6848–6856. DOI: 10.1109/CVPR.2018.00716
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).
Typ dokumentu
Bibliografia
Identyfikator YADDA
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