BREAST CANCER CAD SYSTEM BY USING TRANSFER LEARNING AND ENHANCED ROI
Muayed S AL-HUSEINY
aalhuseiny@uowasity.edu.iqWasit University, Department of Electrical Engineering (Iraq)
Ahmed S SAJIT
Wasit University, College of Engineering (Iraq)
Abstract
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%.
Keywords:
mammography, transfer learning, computer vision, image processingReferences
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
DOI: https://doi.org/10.1016/0165-1684(95)00049-J
Google Scholar
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
DOI: https://doi.org/10.11591/eei.v9i3.2172
Google Scholar
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. https://doi.org/10.11591/ijeecs.v22.i2.pp1078-1086
DOI: https://doi.org/10.11591/ijeecs.v22.i2.pp1078-1086
Google Scholar
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
DOI: https://doi.org/10.1088/1757-899X/928/2/022035
Google Scholar
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
DOI: https://doi.org/10.1016/j.cmpb.2015.12.014
Google Scholar
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
DOI: https://doi.org/10.1007/978-3-030-37218-7_80
Google Scholar
Breast Cancer Facts and Statistics. (2018). Retrieved June 12, 2021 from https://www.breastcancer.org/facts-statistics
Google Scholar
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
Google Scholar
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
DOI: https://doi.org/10.1109/ICOMET.2018.8346384
Google Scholar
Convolutional neural network. (n.d.). Wikipedia Retrieved June 21, 2022 from https://en.wikipedia.org/w/index.php?title=Convolutional_neural_network&oldid=1029918158
Google Scholar
Davis, L. E. (n.d.). What Is a Mammogram? Retrieved June 20, 2021 from https://www.verywellhealth.com/mammogram-what-to-expect-430283
Google Scholar
Deep Learning Network Part Three: GoogLeNet Series. (n.d.). Retrieved June 15, 2021 from https://www.programmersought.com/article/85103454206/
Google Scholar
Gabor filter. (n.d.). Wikipedia. Retrieved June 21, 2022 from https://en.wikipedia.org/w/index.php?title=Gabor_filter&oldid=993157632
Google Scholar
Gonzalez, R. C., & Woods, R. E. (2006). Digital Image Processing (3rd Edition). Prentice-Hall, Inc.
Google Scholar
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.
Google Scholar
Jalalian, A., Mashohor, S. B., Mahmud, H. R., Saripan, M. I., Ramli, A. R., & Karasfi, B. (2013). Computeraided 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
DOI: https://doi.org/10.1016/j.clinimag.2012.09.024
Google Scholar
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
DOI: https://doi.org/10.1117/12.910710
Google Scholar
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
DOI: https://doi.org/10.1109/CVPR.2006.68
Google Scholar
Malgonde, S. (2021). Transfer learning using Tensorflow. https://medium.com/@subodh.malgonde/transferlearning-using-tensorflow-52a4f6bcde3e
Google Scholar
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
DOI: https://doi.org/10.1007/s00521-020-05394-5
Google Scholar
Melli, G. (2021). GoogLeNet. https://www.gabormelli.com/RKB/GoogLeNet
Google Scholar
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
DOI: https://doi.org/10.1093/jnci/dji131
Google Scholar
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
DOI: https://doi.org/10.1007/978-3-319-07887-8_13
Google Scholar
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
DOI: https://doi.org/10.1007/s11263-015-0816-y
Google Scholar
Santos, L. (2019). Artificial Inelligence. GitBook.
Google Scholar
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
DOI: https://doi.org/10.1109/IJCNN.2016.7727519
Google Scholar
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.
DOI: https://doi.org/10.1109/TBME.2015.2496264
Google Scholar
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.
Google Scholar
Survival. (n.d.). Retrieved August 20, 2022 from https://www.cancerresearchuk.org/about-cancer/breast-cancer/survival
Google Scholar
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
DOI: https://doi.org/10.1109/CVPR.2015.7298594
Google Scholar
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
DOI: https://doi.org/10.1109/ICORAS.2017.8308076
Google Scholar
Tripathy, A. (2016). GoogLeNet Insights slideshare.net. https://www.youtube.com/watch?v=_XF7N6rp9Jw
Google Scholar
Written evidence (RTR0073). (2022). Breast Cancer Now and UK Parliament. https://committees.parliament.uk/writtenevidence/42740/pdf/
Google Scholar
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
DOI: https://doi.org/10.32604/cmes.2020.08459
Google Scholar
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
DOI: https://doi.org/10.1007/978-3-030-59338-4_18
Google Scholar
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
DOI: https://doi.org/10.1109/ICCV.2011.6126474
Google Scholar
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
DOI: https://doi.org/10.1364/AO.29.004790
Google Scholar
Authors
Muayed S AL-HUSEINYaalhuseiny@uowasity.edu.iq
Wasit University, Department of Electrical Engineering Iraq
Authors
Ahmed S SAJITWasit University, College of Engineering Iraq
Statistics
Abstract views: 215PDF downloads: 107
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
All articles published in Applied Computer Science are open-access and distributed under the terms of the Creative Commons Attribution 4.0 International License.
Most read articles by the same author(s)
- Noor SABAH, Ekhlas HAMEED, Muayed S AL-HUSEINY, OPTIMAL SLIDING MODE CONTROLLER DESIGN BASED ON WHALE OPTIMIZATION ALGORITHM FOR LOWER LIMB REHABILITATION ROBOT , Applied Computer Science: Vol. 17 No. 3 (2021)
Similar Articles
- Nataliya SHABLIY, Serhii LUPENKO, Nadiia LUTSYK, Oleh YASNIY, Olha MALYSHEVSKA, KEYSTROKE DYNAMICS ANALYSIS USING MACHINE LEARNING METHODS , Applied Computer Science: Vol. 17 No. 4 (2021)
- Thanh-Lam BUI, Ngoc-Tien TRAN, NAVIGATION STRATEGY FOR MOBILE ROBOT BASED ON COMPUTER VISION AND YOLOV5 NETWORK IN THE UNKNOWN ENVIRONMENT , Applied Computer Science: Vol. 19 No. 2 (2023)
- Mahmoud BAKR, Sayed ABDEL-GABER, Mona NASR, Maryam HAZMAN, TOMATO DISEASE DETECTION MODEL BASED ON DENSENET AND TRANSFER LEARNING , Applied Computer Science: Vol. 18 No. 2 (2022)
- Edyta ŁUKASIK, Emilia ŁABUĆ, ANALYSIS OF THE POSSIBILITY OF USING THE SINGULAR VALUE DECOMPOSITION IN IMAGE COMPRESSION , Applied Computer Science: Vol. 18 No. 4 (2022)
- Tomasz NOWICKI, Adam GREGOSIEWICZ, Zbigniew ŁAGODOWSKI, PRODUCTIVITY OF A LOW-BUDGET COMPUTER CLUSTER APPLIED TO OVERCOME THE N-BODY PROBLEM , Applied Computer Science: Vol. 17 No. 4 (2021)
- Benjamin KOMMEY, Ernest Ofosu ADDO, Elvis TAMAKLOE, Eric Tutu TCHAO, Henry NUNOO-MENSAH, A SIX-PORT MEASUREMENT DEVICE FOR HIGH POWER MICROWAVE VECTOR NETWORK ANALYSIS , Applied Computer Science: Vol. 18 No. 3 (2022)
- Behnaz ESLAMI, Mehdi HABIBZADEH MOTLAGH, Zahra REZAEI, Mohammad ESLAMI, Mohammad AMIN AMINI, UNSUPERVISED DYNAMIC TOPIC MODEL FOR EXTRACTING ADVERSE DRUG REACTION FROM HEALTH FORUMS , Applied Computer Science: Vol. 16 No. 1 (2020)
- Zaid ALSAYGH, Zohair AL-AMEEN, CONTRAST ENHANCEMENT OF SCANNING ELECTRON MICROSCOPY IMAGES USING A NONCOMPLEX MULTIPHASE ALGORITHM , Applied Computer Science: Vol. 18 No. 2 (2022)
- Amina KINANE DAOUADJI, Fatima BENDELLA, IMPROVING E-LEARNING BY FACIAL EXPRESSION ANALYSIS , Applied Computer Science: Vol. 20 No. 2 (2024)
- Sri INDRA MAIYANTI, Anita DESIANI, Syafrina LAMIN, P PUSPITAHATI, Muhammad ARHAMI, Nuni GOFAR, Destika CAHYANA, ROTATION-GAMMA CORRECTION AUGMENTATION ON CNN-DENSE BLOCK FOR SOIL IMAGE CLASSIFICATION , Applied Computer Science: Vol. 19 No. 3 (2023)
You may also start an advanced similarity search for this article.