Potential Role of Artificial Intelligence in Breast Cancer Detection- A Review

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
© 2022 by IJETT Journal
Volume-70 Issue-7
Year of Publication : 2022
Authors : Sudha Prathyusha Jakkaladiki, Filip maly
DOI : 10.14445/22315381/IJETT-V70I7P214

How to Cite?

Sudha Prathyusha Jakkaladiki, Filip maly, "Potential Role of Artificial Intelligence in Breast Cancer Detection- A Review" International Journal of Engineering Trends and Technology, vol. 70, no. 7, pp. 130-139, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I7P214

Breast cancer remains a major cause of mortality in females worldwide. Detecting breast cancer at earlier stages would make a significant difference among the global population. Artificial intelligence (AI) has made its way to concern for developing technologies and approaches for detecting cancer at earlier stages, artificial intelligence (AI) has made its way. Recent research by scientific experts has concentrated on this automated process. The major advantages of enhancing the research on this particular field involving AI in detection are due to the usage of deep learning algorithms (software) and the hardware capable of using the complex and complicated algorithms of AI. The advantages also include the accessibility of larger datasets required for AI training approaches. The identification and detection of breast cancer have been performed using mammograms, ultrasound, histopathology, magnetic resonance imaging, or a conjunction of these imaging techniques in an automated manner. Combining image-specific findings and underlying genetic, pathologic, and clinical characteristics in breast cancer is becoming increasingly valuable. Radiologists now have more diagnostic tools and image collections to study and interpret because of the introduction of innovative imaging modalities. Integrating an AI-based workflow into breast imaging allows many data streams to be combined into strong multidisciplinary applications, perhaps leading to tailored patient-specific therapy. The current article analyses the role of AI in the early detection of breast cancer.

Artificial intelligence, Breast cancer, Deep learning, Early detection, Medical imaging.

[1] F. Bray, J. Ferlay, I. Soerjomataram, R. L. Siegel, L. A. Torre, and A. Jemal, "Global Cancer Statistics 2018: Globocan Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries," Ca: A Cancer Journal for Clinicians, Nov. 2018, Vol.68, No.6, Pp.394–424, 2018. Doi: 10.3322/Caac.21492.
[2] R. K. A, R. K, M. Ss, and K. S, "in Vitro and in Silico Anti-Breast Cancer Analysis of Bioactive Metabolites of Bacillus Subtilis Isolated From Soil," Saudi Journal Of Pathology and Microbiology, Apr. 30, 2020, Vol.5, No.4, Pp.220–229,2020. Doi: 10.36348/Sjpm.2020.V05i04.006.
[3] Nih, Cancer Stat Facts: Female Breast Cancer.
[4] S. J. Schnitt, "Pathology of Invasive Breast Cancer," Diseases Of The Breast, 2000.
[5] H. Joshi and M. F. Press, "Molecular Oncology of Breast Cancer," in The Breast, 2018, Elsevier, Pp.282-307, 2018.
[6] H. T. Phung, C. Van Nguyen, N. T. Mai, H. T. N. Vu, K. H. Pham, and G. Le Tran, "Impact of Androgen Receptor Expression and the Ar: Er Ratio on the Survival Outcomes in the Diverse Subgroups of Vietnamese Breast Cancer: A Single Institutional Retrospective Cohort Analysis," Technology In Cancer Research & Treatment, Vol.21, Pp.153303382210809, 2022. Doi: 10.1177/15330338221080941.
[7] M. E. H. Hammond Et Al, "American Society of Clinical Oncology/College of American Pathologists Guideline Recommendations for Immunohistochemical Testing of Estrogen and Progesterone Receptors in Breast Cancer," Journal Of Clinical Oncology, Vol.28, No.16, Pp.2784–2795, 2010. Doi: 10.1200/Jco.2009.25.6529.
[8] M. Tarique, F. Elzahra, A. Hateem, and M. Mohammad, "Fourier Transform Based Early Detection of Breast Cancer By Mammogram Image Processing," Journal Of Biomedical Engineering and Medical Imaging, Vol.2, No.4 ,2015. Doi: 10.14738/Jbemi.24.1308.
[9] American Cancer Society, Breast Cancer Early Detection and Diagnosis.
[10] T. Saba, I. Abunadi, T. Sadad, A. R. Khan, and S. A. Bahaj, "Optimizing the Transfer Learning With Pretrained Deep Convolutional Neural Networks for First Stage Breast Tumor Diagnosis Using Breast Ultrasound Visual Images," Microscopy Research and Technique, Vol.85, No.4, Pp.1444–1453, 2022. Doi: 10.1002/Jemt.24008.
[11] J. Haddadnia, M. Hashemian, and K. Hassanpour, "Diagnosis of Breast Cancer Using A Combination of Genetic Algorithm and Artificial Neural Network in Medical Infrared Thermal Imaging," Iranian Journal Of Medical Physics, Vol.9, No.4, Pp.265–274, 2012.
[12] P. Pendharkar, "Association, Statistical, Mathematical and Neural Approaches for Mining Breast Cancer Patterns, " Expert Systems with Applications,Vol.17, No.3, Pp.223–232, 1999. Doi: 10.1016/S0957-4174(99)00036-6.
[13] D. L. Poole and A. K. Mackworth, Artificial Intelligence: Foundations Of Computational Agents, Cambridge University Press, 2010.
[14] N. Houssami, C. I. Lee, D. S. M. Buist, and D. Tao, "Artificial Intelligence for Breast Cancer Screening"Opportunity Or Hype? The Breast, Vol.36, Pp.31–33, 2017. Doi: 10.1016/J.Breast.2017.09.003.
[15] A. D. Trister, D. S. M. Buist, and C. I. Lee, "Will Machine Learning Tip the Balance in Breast Cancer Screening?," Jama Oncology, Vol.3, No.11, Pp. 1463, 2017. Doi: 10.1001/Jamaoncol.2017.0473.
[16] D. Wang, A. Khosla, R. Gargeya, H. Irshad, and A. H. Beck, "Deep Learning for Identifying Metastatic Breast Cancer," 2016.
[17] S. Mantrala Et Al., "Concordance in Breast Cancer Grading By Artificial Intelligence on Whole Slide Images Compares With A MultiInstitutional Cohort of Breast Pathologists," Archives Of Pathology & Laboratory Medicine, 2022. Doi: 10.5858/Arpa.2021-0299-Oa.
[18] S. Bharti and A. Mcgibney, "Corol: A Reliable Framework for Computation Offloading in Collaborative Robots," Ieee Internet Of Things Journal, Pp.1–1, 2020. Doi: 10.1109/Jiot.2022.3155587.
[19] C. M. Bishop and N. M. Nasrabadi, "Pattern Recognition and Machine Learning," Vol.4, No.4 , 2006.
[20] S. Haykin and R. Lippmann, "Neural Networks, A Comprehensive Foundation," International Journal Of Neural Systems, Vol.5, No.4,Pp.363–364, 1994.
[21] A. Krizhevsky, I. Sutskever, and G. E. Hinton, "Imagenet Classification With Deep Convolutional Neural Networks," Advances In Neural Information Processing Systems, 2012.
[22] Y. Lecun, Y. Bengio, and G. Hinton, "Deep Learning," Nature, Vol.521, No.7553, Pp.436–444, 2015.Doi: 10.1038/Nature14539.
[23] A. Janowczyk and A. Madabhushi, "Deep Learning for Digital Pathology Image Analysis: A Comprehensive Tutorial With Selected Use Cases," Journal Of Pathology Informatics, Vol.7, No.1, Pp.29, 2016.Doi: 10.4103/2153-3539.186902.
[24] J. Chen and C. Srinivas, "Automatic Lymphocyte Detection in H&E Images With Deep Neural Networks," 2016.
[25] E. Garcia, R. Hermoza, C. B. Castanon, L. Cano, M. Castillo, and C. Castanneda, "Automatic Lymphocyte Detection on Gastric Cancer Ihc Images Using Deep Learning," in 2017 Ieee 30th International Symposium On Computer-Based Medical Systems (Cbms), Pp. 200– 204, 2017. Doi: 10.1109/Cbms.2017.94.
[26] S. Sornapudi Et Al., "Deep Learning Nuclei Detection in Digitized Histology Images By Superpixels," Journal Of Pathology Informatics, Vol.9, No.1, Pp.5, 2018. Doi: 10.4103/Jpi.Jpi_74_17.
[27] H. Höfener, A. Homeyer, N. Weiss, J. Molin, C. F. Lundström, and H. K. Hahn, "Deep Learning Nuclei Detection: A Simple Approach Can Deliver State-of-the-Art Results," Computerized Medical Imaging and Graphics, Vol.70, Pp.43–52, 2018. Doi: 10.1016/J.Compmedimag.2018.08.010.
[28] H. Heathfield and N. Kirkham, "A Cooperative Approach To Decision Support in the Differential Diagnosis of Breast Disease," Medical Informatics, Vol.17, No.1, Pp.21–33, 1992. Doi: 10.3109/14639239209012133.
[29] H. A. Heathfield, G. Winstanley, and N. Kirkham, "Computer-Assisted Breast Cancer Grading," Journal Of Biomedical Engineering, Vol.10, No.5, Pp.379–386, 1988. Doi: 10.1016/0141-5425(88)90139-2.
[30] M. S. Leaning, K. E. H. Ng, and D. G. Cramp, "Decision Support for Patient Management in Oncology," Medical Informatics, Jan. Vol.17, No.1, Pp.35–46, 1992. Doi: 10.3109/14639239209012134.
[31] N. Houssami, G. Kirkpatrick-Jones, N. Noguchi, and C. I. Lee, "Artificial Intelligence (Ai) for the Early Detection of Breast Cancer: A Scoping Review To Assess Ai’s Potential in Breast Screening Practice, " Expert Review Of Medical Devices, Vo.16, No.5, Pp.351–362, 2019. Doi: 10.1080/17434440.2019.1610387.