Development of AI Models from Mammography Images with CNN for Early Detection of Breast Cancer
Abstract
Early detection of breast cancer with computer assistance has developed since two decades ago. Artificial intelligence using the convolutional neural network (CNN) method has successfully predicted mammography images with a high level of accuracy similar to human brain learning. The potential of AI models provides opportunities to spot breast cancer cases better. This research aims to develop AI models with CNN using the public DDSM dataset with a sample size of 1871, consisting of 1546 images for training and 325 images for testing. These AI models provided prediction results with different accuracy rate. Increasing the accuracy of the AI model can be done by improving the image quality before the modeling process, increasing the number of datasets, or carrying out a more profound iteration process so that the AI model with CNN can have a better level of accuracy.
References
H. Sung et al., “Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries,” CA. Cancer J. Clin., vol. 71, no. 3, pp. 209–249, 2021, doi: 10.3322/caac.21660.
J. Ferlay et al., “Estimating the global cancer incidence and mortality in 2018: GLOBOCAN sources and methods,” Int. J. Cancer, vol. 144, no. 8, pp. 1941–1953, 2019, doi: 10.1002/ijc.31937.
S. C. B. Mandelbaltt J S, Stout N K, “Collaborative modelling of benefits and harms associated wirh different U.S. breast cancer screening strategies.” .
A. N. Giaquinto et al., “Breast Cancer Statistics, 2022,” CA. Cancer J. Clin., vol. 72, no. 6, pp. 524–541, 2022, doi: 10.3322/caac.21754.
WHO, “Cancer Screening and Early Detection,” WHO, 2010. [Online]. Available: https://www.who.int/europe/news-room/fact-sheets/item/cancer-screening-and-early-detection-of-cancer.
D. Surya Gowri and T. Amudha, “A review on mammogram image enhancement techniques for breast cancer detection,” Proc. - 2014 Int. Conf. Intell. Comput. Appl. ICICA 2014, pp. 47–51, 2014, doi: 10.1109/ICICA.2014.19.
I. dos S. S. V, Sinnicombe, SM Pinto Pereira, VA MCCormack, S Shiel, N Perry, “Full-field digital versus screen-film mammography: comparison within the UK breast screening program and systematic review of published data,” Radiology, vol. 251, no. 2, pp. 347–358, 2009, [Online]. Available: https://www.ncbi.nlm.nih.gov/books/NBK77894/.
S. E. Singletary, “Rating the Risk Factors for Breast Cancer,” Ann. Surg., vol. 237, no. 4, pp. 474–482, 2003, doi: 10.1097/01.SLA.0000059969.64262.87.
V. Bommel, “Interval cancers and bilateral cancers at breast cancer screening Interval Cancers and Bilateral Cancers at Breast Cancer Screening,” no. 2020, 2023, doi: 10.26481/dis.20201203rb.
M. J. M. Broeders, N. C. Onland-Moret, H. J. T. M. Rijken, J. H. C. L. Hendriks, A. L. M. Verbeek, and R. Holland, “Use of previous screening mammograms to identify features indicating cases that would have a possible gain in prognosis following earlier detection,” Eur. J. Cancer, vol. 39, no. 12, pp. 1770–1775, 2003, doi: 10.1016/S0959-8049(03)00311-3.
B. C. Y. R E Bird, T W Wallace, “Analysis of cancers missed at screening mammography,” Radiology, vol. 184, no. 3, pp. 613–617, 1992, doi: https://doi.org/10.1148/radiology.184.3.1509041.
A. S. Majid, E. S. De Paredes, R. D. Doherty, N. R. Sharma, and X. Salvador, “Missed Breast Carcinoma: Pitfalls and Pearls,” Radiographics, vol. 23, no. 4, pp. 881–895, 2003, doi: 10.1148/rg.234025083.
R. D. Rosenberg et al., “Performance benchmarks for screening mammography,” Radiology, vol. 241, no. 1, pp. 55–66, 2006, doi: 10.1148/radiol.2411051504.
Y. Bengio, Y. Lecun, and G. Hinton, “Deep learning for AI,” Commun. ACM, vol. 64, no. 7, pp. 58–65, 2021, doi: 10.1145/3448250.
Y. Lecun, M. ’ Aurelio, and R. Google, “Deep learning tutorial,” Icml, 2013, [Online]. Available: http://yann.lecun.comhttp//www.cs.toronto.edu/~ranzato.
H. G. E. Krizhevsky A, Sutskever I, “ImageNet classification with deep convolutional neutral networks,” Neural Inf. Syst., vol. 25, no. 2, pp. 1–9, 2012, doi: 10.1201/9781420010749.
T. C. & A. K. Krishnamurthy (Dj) Dvijotham, Jim Winkens, Melih Barsbey, Sumedh Ghaisas, Robert Stanforth, Nick Pawlowski, Patricia Strachan, Zahra Ahmed, Shekoofeh Azizi, Yoram Bachrach, Laura Culp, Mayank Daswani, Jan Freyberg, Christopher Kelly, Atilla Kiraly, Timo K, “Enhancing the reability and accuracy of AI-enabled diagnosis via complementary-driven deferral to clinicians,” Nat. Med., vol. 29, pp. 1814–1821, 2023, [Online]. Available: https://www.nature.com/articles/s41591-023-02437-x.
K. Dembrower et al., “Effect of artificial intelligence-based triaging of breast cancer screening mammograms on cancer detection and radiologist workload: a retrospective simulation study,” Lancet Digit. Heal., vol. 2, no. 9, pp. e468–e474, 2020, doi: 10.1016/S2589-7500(20)30185-0.
A. Rodriguez-Ruiz et al., “Stand-Alone Artificial Intelligence for Breast Cancer Detection in Mammography: Comparison With 101 Radiologists,” J. Natl. Cancer Inst., vol. 111, no. 9, pp. 916–922, 2019, doi: 10.1093/JNCI/DJY222.
A. Rodríguez-Ruiz et al., “Detection of breast cancer with mammography: Effect of an artificial intelligence support system,” Radiology, vol. 290, no. 3, 2019, doi: 10.1148/radiol.2018181371.
F. J. Gilbert et al., “Single Reading with Computer-Aided Detection for Screening Mammography,” N. Engl. J. Med., vol. 359, no. 16, pp. 1675–1684, 2008, doi: 10.1056/nejmoa0803545.
R. Hupse, Detection of malignant masses in breast cancer screening by computer assisted decision making. 2012.
P. Wing and M. H. Langelier, “Workforce shortages in breast imaging: Impact on mammography utilization,” Am. J. Roentgenol., vol. 192, no. 2, pp. 370–378, 2009, doi: 10.2214/AJR.08.1665.
A. Culpan, “Radiographer involvement in mammography image interpretation : a survey of United Kingdom practice .,” Radiography, vol. 22, no. 4, pp. 306–312, 2016, doi: https://doi.org/10.1016/j.radi.2016.03.004.
K. K. Evans, R. L. Birdwell, and J. M. Wolfe, “If You Don’t Find It Often, You Often Don’t Find It: Why Some Cancers Are Missed in Breast Cancer Screening,” PLoS One, vol. 8, no. 5, pp. 1–6, 2013, doi: 10.1371/journal.pone.0064366.
S. D. P T Huynh, AM Jarolimek, “The false-negative mammogram,” RadioGraphics, vol. 18, no. 5, 1998, doi: htttps://doi.org/10.1148/radiographic.18.5.9747612.
P. K. J. M. Health, K. Bowyer, D. Kopans, R. Moore, “The digital database for screening mammography,” in Digital Mammography, 1998, [Online]. Available: https://link.springer.com/chapter/10.1007/978-94-011-5318-8_75.
University of South Florida, “DDSM : Digital Database for Screening Mammography,” http://www.eng.usf.edu/, 2012. http://www.eng.usf.edu/cvprg/mammography/database.html.
Y. E. Almalki, T. A. Soomro, M. Irfan, S. K. Alduraibi, and A. Ali, “Impact of image enhancement module for analysis of mammogram images for diagnostics of breast cancer,” Sensors, vol. 22, no. 1868, pp. 1–20, 2022, doi: 10.3390/healthcare10050801.
A. Mračko, L. Vanovčanová, and I. Cimrák, “Mammography Datasets for Neural Networks—Survey,” J. Imaging, vol. 9, no. 5, 2023, doi: 10.3390/jimaging9050095.
Z. Jiao, X. Gao, Y. Wang, and J. Li, “A deep feature based framework for breast masses classification,” Neurocomputing, vol. 197, pp. 221–231, 2016, doi: 10.1016/j.neucom.2016.02.060.
C. Songsaeng, P. Woodtichartpreecha, and S. Chaichulee, “Multi-Scale Convolutional Neural Networks for Classification of Digital Mammograms with Breast Calcifications,” IEEE Access, vol. 9, pp. 114741–114753, 2021, doi: 10.1109/ACCESS.2021.3104627.
S. Gaur, V. Dialani, P. J. Slanetz, and R. L. Eisenberg, “Architectural distortion of the breast,” Am. J. Roentgenol., vol. 201, no. 5, pp. 662–670, 2013, doi: 10.2214/AJR.12.10153.
Johnson B, “Asymmetries in mammography,” Radiol. Technol., vol. 92, pp. 281M-298M, 2021, [Online]. Available: http://www.radiologictechnology.org/content/92/3/281M.full.
J. H. Youk, E. K. Kim, K. H. Ko, and M. J. Kim, “Asymmetric mammographic findings based on the fourth edition of BI-RADS: types, evaluation, and management.,” Radiographics, vol. 29, no. 1, 2009, doi: 10.1148/rg.e33.
D. A. Spak, J. S. Plaxco, L. Santiago, M. J. Dryden, and B. E. Dogan, “BI-RADS® fifth edition: A summary of changes,” Diagn. Interv. Imaging, vol. 98, no. 3, pp. 179–190, 2017, doi: 10.1016/j.diii.2017.01.001.
Y. Qiu et al., “An initial investigation on developing a new method to predict short-term breast cancer risk based on deep learning technology,” Med. Imaging 2016 Comput. Diagnosis, vol. 9785, p. 978521, 2016, doi: 10.1117/12.2216275.
T. Fujioka et al., “Distinction between benign and malignant breast masses at breast ultrasound using deep learning method with convolutional neural network,” Jpn. J. Radiol., no. 0123456789, 2019, doi: 10.1007/s11604-019-00831-5.
D. Ribli, A. Horváth, Z. Unger, P. Pollner, and I. Csabai, “Detecting and classifying lesions in mammograms with Deep Learning,” Sci. Rep., vol. 8, no. 1, pp. 1–7, 2018, doi: 10.1038/s41598-018-22437-z.
O. I. Ozsahin D U, Emegano D I, Uzun B, “The systematic review of artificial intelligence application in breast cancer diagnosis,” Diagnostics, vol. 13, no. 45, pp. 2–18, 2022, doi: https://doi.org/10.3390/diagnostics13010045.
B. J. Erickson, P. Korfiatis, Z. Akkus, and T. L. Kline, “Machine learning for medical imaging,” Radiographics, vol. 37, no. 2, pp. 505–515, 2017, doi: 10.1148/rg.2017160130.
K. Yasaka, H. Akai, A. Kunimatsu, S. Kiryu, and O. Abe, “Deep learning with convolutional neural network in radiology,” Jpn. J. Radiol., vol. 36, no. 4, pp. 257–272, 2018, doi: 10.1007/s11604-018-0726-3.
P. Lakhani and B. Sundaram, “Deep learning at chest radiography: Automated classification of pulmonary tuberculosis by using convolutional neural networks,” Radiology, vol. 284, no. 2, pp. 574–582, 2017, doi: 10.1148/radiol.2017162326.
K. Yasaka, H. Akai, A. Kunimatsu, S. Kiryu, and O. Abe, “Deep learning with convolutional neural network in radiology,” Jpn. J. Radiol., vol. 36, no. 4, pp. 257–272, 2018, doi: 10.1007/s11604-018-0726-3.
geeksforgeeks.org, “Introduction to convolutional neural network,” www.geeksforgeeks.org, 2023. https://www.geeksforgeeks.org/introduction-convolution-neural-network/.
K. Neshatpour, H. Homayoun, and A. Sasan, “ICNN: The iterative convolutional neural network,” ACM Trans. Embed. Comput. Syst., vol. 18, no. 6, 2019, doi: 10.1145/3355553.
D. A. Anam, L. Novamizanti, and S. Rizal, “KLASIFIKASI PATOLOGI MAKULA PADA RETINA BERDASARKAN CITRA RETINAL OCT MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORK (Classifying Retinal Pathology Using OCT Retinal Imaging With Convolutional Neural Network),” e-Proceeding Eng., vol. 8, no. 5, pp. 5072–5082, 2021, [Online]. Available: https://www.kaggle.com/paultimothymooney/kermany2018.
D. S. Candra, “Implementasi Convolutional Neural Network (CNN) Untuk Klasifikasi Citra Bunga,” vol. 16, no. 1, pp. 2580–2582, 2020.
K. J. Tsai et al., “A High-Performance Deep Neural Network Model for BI-RADS Classification of Screening Mammography,” Sensors, vol. 22, no. 3, 2022, doi: 10.3390/s22031160.
Copyright (c) 2024 Nurbaiti Nurbaiti, Eka Putra Syarif Hidayat, Khairil Anwar, Dudung Hermawan, Salman Izzuddin
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Authors who publish with this journal agree to the following terms:
1. Copyright on any article is retained by the author(s).
2. The author grants the journal, right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work’s authorship and initial publication in this journal.
3. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal’s published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
4. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.
5. The article and any associated published material is distributed under the Creative Commons Attribution-ShareAlike 4.0 International License
