Transfer Learning based Breast Cancer Classification via Deep Convolutional Neural Network

Transfer Learning based Breast Cancer Classification via Deep Convolutional Neural Network

Автор: Markos Wondim Walle, Kula Kakeba Tune, Natnael Tilahun Sinshaw, Sudhir Kumar Mohapatra

Журнал: International Journal of Engineering and Manufacturing @ijem

Статья в выпуске: 4 vol.13, 2023 года.

Бесплатный доступ

Breast cancer is a leading cause of death among women, and the subjectivity of human visual perception and lack of automated detection methods can lead to misclassification of breast cancer images. In this study, a breast cancer classification model using a Convolutional Neural Network (CNN) deep learning algorithm was proposed. The model demonstrated high accuracy in classifying breast images as benign or malignant, with a classification accuracy of 97.1%. The model was also able to run on low computational resources. The study used a dataset of 2009 breast images labeled by two radiologists and included six scenarios based on different hyperparameters, augmentation values, pretrained models, and models built from scratch. While the performance of the proposed model was promising, further improvement may be achieved by using a larger breast image dataset and a machine with more powerful GPU hardware.

Еще

Deep Learning, Convolutional Neural Network, Breast Cancer Classification, Benign, Malignant

Короткий адрес: https://sciup.org/15018704

IDR: 15018704   |   DOI: 10.5815/ijem.2023.04.04

Список литературы Transfer Learning based Breast Cancer Classification via Deep Convolutional Neural Network

  • MahletKifle et al., “Disease prevention and control directorate,” FEDERAL MINISTRY OF HEALTH ETHIOPIA, 2020. [Online]. Available: https://www.iccp-portal.org/sites/default/files/plans. Accessed on: April 10, 2020.
  • N. Saffari, H. A. Rashwan, M. Abdel-Nasser, V. Kumar Singh, M. Arenas, E. Mangina, B. Herrera, and D. Puig, “Fully automated breast density segmentation and classification using deep learning,” Diagnostics, vol. 10, no. 11, p. 988, 2020.
  • Cancer.org, “How common is breast cancer?” American Cancer Society, 2022. [Online]. Available: https://www.cancer.org/cancer/breast-cancer/.Accessed on: April 1, 2020.
  • A. M. T. W. Abate SM, Yilma Z, “Trends of breast cancer in ethiopia,” Int J Cancer Res Mol Mech, vol. 2(1), 2016.
  • R. Dhivya and R. Dharani, “Survey on breast cancer detection using neural networks,” Forest, vol. 650, pp. 96–777, 2019.
  • N. M. S. H. R. E. H. M. A. M. L. M. B. K. D. B. E. P. R . S.E. A. Monticciolo, D. L., “Breast cancer screening for average-risk women: Recommendations from the acr commission on breast imaging,” Journal of the American College of Radiology : JACR, vol. 14, no. 9, p. 1137–1143, 2017.
  • E. Rashed and M. S. A. El Seoud, “Deep learning approach for breast cancer diagnosis,” in Proceedings of the 2019 8th International Conference on Software and Information Engineering, 2019, pp. 243–247.
  • K. J. Geras, S. Wolfson, Y. Shen, N. Wu, S. Kim, E. Kim, L. Heacock, U. Parikh, L. Moy, and K. Cho, “High-resolution breast cancer screening with multi-view deep convolutional neural networks,” arXiv preprint arXiv:1703.07047, 2017.
  • S. H. Kassani, P. H. Kassani, M. J. Wesolowski, K. A. Schneider, and R. Deters, “Classification of histopathological biopsy images using ensemble of deep learning networks,” arXiv preprint arXiv:1909.11870, 2019.
  • T. G. Debelee, F. Schwenker, A. Ibenthal, and D. Yohannes, “Survey of deep learning in breast cancer image analysis,” Evolving Systems, vol. 11, no. 1, pp. 143–163, 2020.
  • T. G. Debelee, A. Gebreselasie, F. Schwenker, M. Amirian, and D. Yohannes, “Classification of mammograms using texture and cnn based extracted features,” in Journal of Biomimetics, Biomaterials and Biomedical Engineering, vol. 42. Trans Tech Publ, 2019, pp. 79–97.
  • D. A. Ragab, M. Sharkas, S. Marshall, and J. Ren, “Breast cancer detection using deep convolutional neural networks and support vector machines,” PeerJ, vol. 7, p. e6201, 2019.
  • Mohapatra, Sudhir Kumar. "Automatic Lung Tuberculosis Detection Model Using Thorax Radiography Image." Deep Learning Applications in Medical Imaging. IGI Global, 2021. 223-242.
  • Debata, Biswaranjan, Sudhira Kumar Mohapatra, and Rojalina Priyadarshini. "A Systematic Literature Review on Pulmonary Disease Detection Using Machine Learning." Proceedings of the International Conference on Cognitive and Intelligent Computing: ICCIC 2021, Volume 2. Singapore: Springer Nature Singapore, 2023.
  • Mohapatra, Sudhir Kumar, Beakal Gizachew Assefa, and Getamesay Belayneh. "A SVM Based Model for COVID Detection Using CXR Image." Advances of Science and Technology: 9th EAI International Conference, ICAST 2021, Hybrid Event, Bahir Dar, Ethiopia, August 27–29, 2021, Proceedings, Part I. Springer International Publishing, 2022.
  • Mekonnen, A. A., Seid, H. W., Mohapatra, S. K., & Prasad, S. (2021). Developing Brain Tumor Detection Model Using Deep Feature Extraction via Transfer Learning. In Handbook of Research on Automated Feature Engineering and Advanced Applications in Data Science (pp. 119-137). IGI Global.
  • Mohapatra, Sudhir Kumar, Srinivas Prasad, and Sarat Chandra Nayak. "Wheat Rust Disease Detection Using Deep Learning." Data Science and Data Analytics: Opportunities and Challenges (2021): 191
  • Sinshaw, Natnael Tilahun, Beakal Gizachew Assefa, and Sudhir Kumar Mohapatra. "Transfer Learning and Data Augmentation Based CNN Model for Potato Late Blight Disease Detection." 2021 International Conference on Information and Communication Technology for Development for Africa (ICT4DA). IEEE, 2021.
  • W. E. Fathy and A. S. Ghoneim, “A deep learning approach for breast cancer mass detection,” Int. J. Adv. Comput. Sci. Appl, vol. 10, no. 1, pp. 175–182, 2019.
  • G. Liang, X. Wang, Y. Zhang, X. Xing, H. Blanton, T. Salem, and N.Jacobs, “Joint 2d-3d breast cancer classification,” 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 692–696, 2019.
  • P. Gorgel, A. Sertbas, and O. N. Uc¸an, “Computer-aided classification of breast masses in mammogram images based on spherical wavelet transform and support vector machines,” Expert Systems, vol. 32, no. 1, pp. 155–164, 2015.
  • A. Alarabeyyat, M. Alhanahnah et al., “Breast cancer detection using knearest neighbor machine learning algorithm,” in 2016 9th International Conference on Developments in eSystems Engineering (DeSE). IEEE, 2016, pp. 35–39.
  • D. Bazazeh and R. Shubair, “Comparative study of machine learning algorithms for breast cancer detection and diagnosis,” in 2016 5th international conference on electronic devices, systems and applications (ICEDSA). IEEE, 2016, pp. 1–4.
  • E. W. Weisstein, “Convolution,” mathworld.wolfram.com, 2020. [Online]. Available: https://mathworld.wolfram.com/Convolution.html. Acessed on: April 30, 2020.
  • A. Zhang, Z. C. Lipton, M. Li, and A. J. Smola, “Dive into deep learning,” arXiv preprint arXiv:2106.11342, 2021.
  • Y. K. Afework and T. G. Debelee, “Detection of bacterial wilt on enset crop using deep learning approach,” International Journal of Engineering Research in Africa, vol. 51, pp. 131–146, 12 2020.
  • M. Talo, U. B. Baloglu, O. Yıldırım, and U. R. Acharya, “Application of deep transfer learning for automated brain abnormality classification using MR images,” Cognitive Systems Research, vol. 54, pp. 176–188, 2019.
  • O. Yildirim, M. Talo, B. Ay, U. B. Baloglu, G. Aydin, and U. R. Acharya, “Automated detection of diabetic subject using pre-trained 2dcnn models with frequency spectrum images extracted from heart rate signals,” Computers in biology and medicine, vol. 113, p. 103387, 2019.
  • M. Geng, Y. Wang, T. Xiang, and Y. Tian, “Deep transfer learning for person re-identification,” arXiv preprint arXiv:1611.05244, 2016.
  • R. Mehra, “Breast cancer histology images classification: Training from scratch or transfer learning?” ICT Express, vol. 4, no. 4, pp. 247–254, 2018
  • J. Brownlee, Machine learning mastery with Python: understand your data, create accurate models, and work projects end-to-end. Machine Learning Mastery, 2016.
  • A. C. Wilson, R. Roelofs, M. Stern, N. Srebro, and B. Recht, “The marginal value of adaptive gradient methods in machine learning,” Advances in neural information processing systems, vol. 30, 2017.
  • N. Ketkar and E. Santana, Deep learning with Python. Springer, 2017, vol.1.
Еще
Статья научная
QR-код для установки приложения SciUp Наведите камеру и скачайте бесплатное приложение SciUp