CFP last date
20 January 2025
Reseach Article

Automatic Detection of Breast Cancer using Deep Learning

by Harshit Bharti, Jagdish Raikwal, Meena Sharma
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 185 - Number 31
Year of Publication: 2023
Authors: Harshit Bharti, Jagdish Raikwal, Meena Sharma
10.5120/ijca2023923069

Harshit Bharti, Jagdish Raikwal, Meena Sharma . Automatic Detection of Breast Cancer using Deep Learning. International Journal of Computer Applications. 185, 31 ( Aug 2023), 11-18. DOI=10.5120/ijca2023923069

@article{ 10.5120/ijca2023923069,
author = { Harshit Bharti, Jagdish Raikwal, Meena Sharma },
title = { Automatic Detection of Breast Cancer using Deep Learning },
journal = { International Journal of Computer Applications },
issue_date = { Aug 2023 },
volume = { 185 },
number = { 31 },
month = { Aug },
year = { 2023 },
issn = { 0975-8887 },
pages = { 11-18 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume185/number31/32891-2023923069/ },
doi = { 10.5120/ijca2023923069 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:27:33.013504+05:30
%A Harshit Bharti
%A Jagdish Raikwal
%A Meena Sharma
%T Automatic Detection of Breast Cancer using Deep Learning
%J International Journal of Computer Applications
%@ 0975-8887
%V 185
%N 31
%P 11-18
%D 2023
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Breast cancer is being discovered in women with cancer at an alarmingly high rate in India. In the future, these numbers will significantly rise, and the majority of Indians between the ages of 20 and 34 will see this increase. Since a late-stage diagnosis reduces the probability of a cure, breast cancer claims more lives among women globally than any other disease. The processing and learning capabilities of AI have increased recently. Using photos, researchers are able to detect cancer using machine learning. Furthermore, judging the health of tissues using digital images provides a second opinion faster. Here, our attention will be on applying Keras and deep learning techniques to identify cancer using histopathology pictures.

References
  1. Zejmo, M., Kowal, M., Korbicz, J.,& Monczak, R. 2017.“Classification of breast cancer cytological specimen using convolutional neural network”. Journal of Physics: Conference Series, 783(1), 012060.
  2. Lima, S. M. L., Silva-Filho, A. G., & Santos, W. P. 2016. “Detection and classification of masses in mammography images in a multi-kernel approach”. Computer Methods and Programs in Biomedicine, 134, 11-29.
  3. Chen, D. R., Chang, R. F., Huang, Y. L., Chou, Y. H., Tiu, C. M., & Tsai, P. P. 2000.“Texture analysis of breast tumors on sonograms. Seminars in Ultrasound, CT and MRI,” 21(4), 308-316.
  4. Vaka, A. R., Sonia, B., & Sudheer Reddy, K. 2020.“Breast cancer detection by leveraging Machine Learning”. ICT Express, 6(2), 320-324.
  5. Chen, D. R., Chang, R. F., & Huang, Y. L.1999. “Computer-aided diagnosis applied to US of solid breast nodules by using neural networks. Radiology”, 213(2), 407-412.
  6. Kaggle (2020). “Breast Histopathology Images Dataset”. Retrieved http://www.kaggle.com/dataset/paultimothymooney/bre ast-histopathology-images.
  7. Chen, D.R., Chang, R.F., Huang, Y.L., Chou, Y.H., Tiu, C.M., & Tsai, P.P. 2020.“Analysis of breast tumors on sonograms. Seminars in Ultrasound, CT and MRI”, 21(4), 308-316.
  8. Alanazi, S.A., Kamruzzaman, M.M., Sarker, M.N.I., Alruwaili, M., Alhwaiti, Y., Alshammari, N., & Siddiqi, M.H. 2021.“Boosting Breast Cancer Detection Using Constitutional Neural Network. Journal of Healthcare Engineering”, 5528622, 11 pages.
  9. Rezaeilouyeh, H., Mollahusseini, A., & Masoor, M.H. 2016. “Microscopic medical image classification framework via deep learning and Shearlet transform”. Journal of Medical Imaging, 3(4), 044501.
  10. Lessa, V., & Mar-engoni, “M. 2016. “Applying Artificial Neural Network for the Classification of Breast Cancer Using Infrared Thermographic Images”. In Proceedings of the International Conference on Biomedical Engineering Systems and Technologies (pp. 429-438). Springer International Publishing.
  11. Guan H, Sun Y, et al. 2009. “Breast cancer detection using molecular and pathological analysis”. Med Mol Res, 7(1), 193-200. ASCO (American Society of Clinical Oncology).DOI: 10.3892/mmr_00000193.
  12. Kooi, T., Litjens, G., & Ginneken, B. 2017. “Large scale deep learning for computer aided detection of mammography lesions”. Medical Image Analysis, 35, 303-312.
  13. Maan, J., & Maan, H. 2022.“Breast Cancer Detection using Histopathology Images”. International Journal of Computer Science Trends and Technology (IJCST), 10. Retrieved from arXiv preprint arXiv:2202.06109.
  14. M. Masud, A. E. Eldin Rashed, and M. S. Hossain, 2020. "Convolutional neural network-based models for diagnosis of breast cancer," Neural Computing and Applications, vol. 5.
  15. Cruz-Roa, A., Basavanhally, A., González, F. A., Gilmore, H., Feldman, M., Ganesan, S., ... & Madabhushi, A. 2014. “Automatic detection of invasive ductal carcinoma in whole slide images with convolutional neural networks…..Digital Pathology (Vol. 9041, p. 904103)”. International Society for Optics and Photonics.
  16. Spanhol, F. A., Oliveira, L. S., Petitjean, C., & Heutte, L. 2016. “A dataset for breast cancer histopathological image classification”. IEEE Transactions on Biomedical Engineering, 63(7), 1455-1462.
  17. Araújo, T., Aresta, G., Castro, E., Rouco, J., Aguiar, P., Eloy, C., .& Campilho, 2017. “Classification of breast cancer histology images using Convolutional Neural Networks.” PloS One, 12(6), e0177544.
  18. Wang, H., Cruz-Roa, A., Basavanhally, A., Gilmore, H., Shih, N. N., Feldman, M., ... & Gurcan, M. N. 2014. “Mitosis detection in breast cancer pathology images by combining handcrafted and convolutional neural network features”. Journal of Medical Imaging, 1(3), 034003.
  19. Kaur, S., Chakraborty, C., & Kolekar, M. H.2019. “A review of breast cancer histopathological image analysis for detection, diagnosis, and grading”. Journal of Medical Systems, 43(7), 1-18.
  20. Hamidinekoo, A., Denton, E., Rampun, A., Honnor, K., Zwiggelaar, R., & Zwiggelaar, R. 2018. “ Deep learning in mammography and breast histology, an overview and future trends”. Medical Image Analysis, 47, 45.
Index Terms

Computer Science
Information Sciences

Keywords

Breast Cancer Detection Convolution Neural Network (CNN) Invasive Ductile Carcinoma (IDC).