Breast cancer cad system by using transfer learning and enhanced ROI

• Autorzy: Al-Huseiny Muayed S , Sajit Ahmed S

Identyfikatory

DOI

10.35784/acs-2022-8

• Języki publikacji: EN

Abstrakty

EN

Computer systems are being employed in specialized professions such as medical diagnosis to alleviate some of the costs and to improve dependability and scalability. This paper implements a computer aided breast cancer diagnosis system. It utilizes the publicly available mini MIAS mammography image dataset. Images are preprocessed to clean isolate breast tissue region. Extracted regions are used to adjust and verify a pretrained convolutional deep neural network, the GoogLeNet. The implemented model shows good performance results compared to other published works with accuracy of 86.6%, sensitivity of 75% and specificity of 88.9%.

Słowa kluczowe

EN

mammography; transfer learning; computer vision; image processing

• Wydawca: Polskie Towarzystwo Promocji Wiedzy ; Lublin University of Technology
• Czasopismo: Applied Computer Science
• Rocznik: 2022
• Tom: Vol. 18, no 1
• Strony: 99--111
• Opis fizyczny: Bibliogr. 37 poz., fig., tab.

Twórcy

autor

Al-Huseiny Muayed S

• Wasit University, Department of Electrical Engineering, Iraq

• https://orcid.org/0000-0003-3192-0232

autor

Sajit Ahmed S

• Wasit University, College of Engineering, Iraq

• https://orcid.org/0000-0002-1894-9033

Bibliografia

• [1] Aach, T., Kaup, A., & Mester, R. (1995). On texture analysis: Local energy transforms versus quadrature filters. Signal Processing, 45(2), 173-181. https://doi.org/10.1016/0165-1684(95)00049-J
• [2] AL-Huseiny, M. S., Abbas, N. K., & Sajit, A. S. (2020). Diagnosis of arrhythmia based on ECG analysis using CNN. Bulletin of Electrical Engineering and Informatics, 9(3), 988–995. https://doi.org/10.11591/eei.v9i3.2172
• [3] AL-Huseiny, M. S., & Sajit, A. S. (2021). Transfer learning with GoogLeNet for detection of lung cancer. Indonesian Journal of Electrical Engineering and Computer Science, 22(2), 1078–1086. http:/doi.org/10.11591/ijeecs.v22.i2.pp1078-1086
• [4] Al-Yasriy, H. F., Al-Husieny, M. S., Mohsen, F. Y., Khalil, E. A., & Hassan, Z. S. (2020). Diagnosis of lung cancer based on CT scans using CNN. IOP Conference Series: Materials Science and Engineering, 928, 022035. https://doi.org/10.1088/1757-899x/928/2/022035
• [5] Arevalo, J., González, F. A., Ramos-Pollán, R., Oliveira, J. L., & Guevara Lopez, M. A. (2016). Representation learning for mammography mass lesion classification with convolutional neural networks. Computer Methods and Programs in Biomedicine, 127, 248–257. https:/doi.org/10.1016/j.cmpb.2015.12.014
• [6] Batra, K., Sekhar, S., & Radha, R. (2020). Breast cancer detection using CNN on mammogram images. Computational Vision and Bio-Inspired Computing (pp. vol 1108). Springer. https://doi.org/10.1007/978-3-030-37218-7_80
• [7] Breast Cancer Facts and Statistics. (2018). Retrieved June 12, 2021 from https://www.breastcancer.org/facts-statistics
• [8] Breast cancer: prevention and control. (2008). World Health Organisation. https://www.who.int/cancer/detection/breastcancer/en/index1.html#:*:text=Breast%20cancer%0survival%20rates%20vary,et%20al.%2C%202008
• [9] Charan, S., Khan, M. J., & Khurshid, K. (2018). Breast cancer detection in mammograms using convolutional neural network. 2018 International Conference on Computing, Mathematics and Engineering Technologies (iCoMET) (pp. 1–5). IEEE. https://doi.org/10.1109/ICOMET.2018.8346384
• [10] Convolutional neural network. (n.d.). Wikipedia Retrieved June 21, 2022 from https://en.wikipedia.org/w/index.php?title=Convolutional_neural_network&oldid=1029918158
• [11] Davis, L. E. (n.d.). What Is a Mammogram? Retrieved June 20, 2021 from https://www.verywellhealth.com/ mammogram-what-to-expect-430283
• [12] Deep Learning Network Part Three: GoogLeNet Series. (n.d.). Retrieved June 15, 2021 from https://www.programmersought.com/article/85103454206/
• [13] Gabor filter. (n.d.). Wikipedia. Retrieved June 21, 2022 from https://en.wikipedia.org/w/index.php?title=Gabor_filter& oldid=993157632
• [14] Gonzalez, R. C., & Woods, R. E. (2006). Digital Image Processing (3rd Edition). Prentice-Hall, Inc.
• [15] Grgic, M., Delac, K., Bozek, J., & Rangayyan, R. M. (2021). Mammographic image analysis homepage. Video Communications Laboratory (VCL), Faculty of Electrical Engineering and Computing, University of Zagreb, Croatia.
• [16] Jalalian, A., Mashohor, S. B., Mahmud, H. R., Saripan, M. I., Ramli, A. R., & Karasfi, B. (2013). Computer-aided detection/diagnosis of breast cancer in mammography and ultrasound: a review. Clinical Imaging, 37(3), 420-426. https://doi.org/10.1016/j.clinimag.2012.09.024
• [17] Jamieson, A. R., Drukker, K., & Giger, M. L. (2012). Breast image feature learning with adaptive deconvolutional networks. Proc. SPIE 8315, Medical Imaging 2012: Computer-Aided Diagnosis (no. 831506). https://doi.org/10.1117/12.910710
• [18] Lazebnik, S., Schmid, C., & Ponce, J. (2006). Beyond Bags of features: spatial pyramid matching for recognizing natural scene categories. 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06) (pp. 2169-2178). IEEE. https://doi.org/10.1109/CVPR.2006.68
• [19] Malgonde, S. (2021). Transfer learning using Tensorflow. https://medium.com/@subodh.malgonde/transfer-learning-using-tensorflow-52a4f6bcde3e
• [20] Masud, M., Eldin Rashed, A. E., & Hossain, M. S. (2020). Convolutional neural network-based models for diagnosis of breast cancer. Neural Computing and Applications. Springer. https://doi.org/10.1007/s00521-020-05394-5
• [21] Melli, G. (2021). GoogLeNet. https://www.gabormelli.com/RKB/GoogLeNet
• [22] Otten, J. D. M., Karssemeijer, N., Hendriks, J. H. C. L., Groenewoud, J. H., Fracheboud, J., Verbeek, A. L. M., de Koning, H. J., & Holland, R. (2005). Effect of recall rate on earlier screen detection of breast cancers based on the dutch performance indicators. JNCI: Journal of the National Cancer Institute, 97(10), 748–754. https://doi.org/https://10.1093/jnci/dji131
• [23] Petersen, K., Nielsen, M., Diao, P., Karssemeijer, N., & Lillholm, M. (2014). Breast tissue segmentation and mammographic risk scoring using deep learning. Breast Imaging. IWDM 2014. Lecture Notes in Computer Science (vol 8539). Springer. https://doi.org/10.1007/978-3-319-07887-8_13
• [24] Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A. C., & Fei-Fei, L. (2015). ImageNet large scale visual recognition challenge. International Journal of Computer Vision, 115(3), 211-252. https://doi.org/10.1007/s11263-015-0816-y
• [25] Santos, L. (2019). Artificial Inelligence. GitBook.
• [26] Spanhol, F. A., Oliveira, L. S., Petitjean, C., & Heutte, L. (2016a). Breast cancer histopathological image classification using Convolutional Neural Networks. 2016 International Joint Conference on Neural Networks (IJCNN) (pp. 2560-2567). IEEE. https://doi.org/10.0.4.85/IJCNN.2016.7727519
• [27] Spanhol, F. A., Oliveira, L. S., Petitjean, C., & Heutte, L. (2016b). A Dataset for Breast Cancer Histopathological Image Classification. IEEE Transactions on Biomedical Engineering (TBME), 63(7), 1455–1462.
• [28] Suckling, J., Astley, S., Betal, D., Cerneaz, N., Dance, D. R., Kok, S.-L., Parker, J., Ricketts, I., Savage, J., Stamatakis, E., & Taylor, P. (1994). The Mammographic Image Analysis Society Digital Mammogram Database Exerpta Medica. International Congress Series.
• [29] Survival. (n.d.). Retrieved August 20, 2022 from https://www.cancerresearchuk.org/about-cancer/breast-cancer/survival
• [30] Szegedy, C., Wei, L., Yangqing, J., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015). Going deeper with convolutions. 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 1–9). IEEE. https://doi.org/10.1109/CVPR.2015.7298594
• [31] Tan, Y. J., Sim, K. S., & Ting, F. F. (2017). Breast cancer detection using convolutional neural networks for mammogram imaging system. 2017 International Conference on Robotics, Automation and Sciences (ICORAS) (pp. 1–5). IEEE. https://doi.org/10.1109/ICORAS.2017.8308076
• [32] Tripathy, A. (2016). GoogLeNet Insights slideshare.net. https://www.youtube.com/watch?v=_XF7N6rp9Jw
• [33] Written evidence (RTR0073). (2022). Breast Cancer Now and UK Parliament. https://committees.parliament.uk/writtenevidence/42740/pdf/
• [34] Yadav, S.-P., & Yadav, S. (2020). Fusion of medical images in wavelet domain: a hybrid implementation. Computer Modeling in Engineering & Sciences, 122(1), 303-321. https://doi:10.32604/cmes.2020.08459
• [35] Zainudin, Z., Shamsuddin, S. M., & Hasan, S. (2021). Deep layer convolutional neural network (CNN) Architecture for breast cancer classification using histopathological images. In A. E. Hassanien (Ed.), Machine Learning and Big Data Analytics Paradigms: Analysis, Applications and Challenges (pp. 347–364). Springer Nature Switzerland. https:/doi.org/10.1007/978-3-030-59338-4_18
• [36] Zeiler, M. D., Taylor, G. W., & Fergus, R. (2011). Adaptive deconvolutional networks for mid and high level feature learning. 2011 International Conference on Computer Vision (pp. 2018–2025). IEEE. https://doi.org/10.1109/ICCV.2011.6126474
• [37] Zhang, W. (1990). Parallel distributed processing model with local space-invariant interconnections and its optical architecture. Applied Optics, 29(32), 4790–4796. https://doi.org/10.1364/AO.29.004790