Breast Cancer Detection on Mammographic Images using Hyper Parameter Tuning & Optimization: A Convolutional Neural Network & Transfer Learning Approach

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
© 2022 by IJETT Journal
Volume-70 Issue-9
Year of Publication : 2022
Authors : Pratheep Kumar P, V. Mary Amala Bai
DOI : 10.14445/22315381/IJETT-V70I9P208

How to Cite?

Pratheep Kumar P, V. Mary Amala Bai, "Breast Cancer Detection on Mammographic Images using Hyper Parameter Tuning & Optimization: A Convolutional Neural Network & Transfer Learning Approach," International Journal of Engineering Trends and Technology, vol. 70, no. 9, pp. 76-92, 2022. Crossref,

Breast cancer is probably the most well-known; it is also the leading cause of death in women worldwide. Incidentally, it can be cured if discovered early enough. The key issues for rural locations are that radiologists are significantly less likely to identify Breast Cancer (BC) utilizing mammography pictures in testing camps and that early diagnosis of these cancers is more important for all medical specialists. As a result, this study proposes an advanced deep learning-based tool for BC, with the following steps: a) Data Source: The Mammographic Image Analysis Society (MIAS) database of digital mammograms (v1.21), which contains 322 pictures and also real-world data from VPS Lakeshore Hospital Kochi which contains 4118 images, b) Filtering and histogram-based approach for preprocessing, c) feature extraction using a convolutional autoencoder for extracting features from the input, d) feature selection with Recursive Feature Elimination (RFE) for dimensionality reduction, and e) classification using a convolutional neural network with the help of transfer learning. Experiments are performed on various state-of-the-art models, and the suggested model outperforms in various measures (accuracy;0.96, precision;0.95, sensitivity;0.97, specificity:0.98).

Breast Cancer, Classification, Convolutional Neural Network, Deep learning, Mammogram, Transfer Learning.

[1] J Arevalo, Gonz alez fa, Ramos-poll an r, Oliveira jl, Guevara lopez ma, “Representation Learning for Mammography Mass Lesion Classification with Convolutional Neural Networks,” Comput Methods Programs Biomed, vol. 127, pp. 248257, 2016.
[2] Benjamin Q Huynh, Hui Li, and Maryellen L Giger, “Digital Mammographic Tumor Classification using Transfer Learning from Deep Convolutional Neural Networks,” Journal of Medical Imaging, vol. 3, no. 3, pp. 034501, 2016.
[3] Yuan-Pin Lin and Tzyy-Ping Jung, “Improving EEG-based Emotion Classification using Conditional Transfer Learning,” Frontiers in Human Neuroscience, vol. 11, no. 334, 2017.
[4] Christian Szegedy, Wei Liu, Yangqing Jia, Pierre Sermanet, Scott Reed, Dragomir Anguelov, Dumitru Erhan, Vincent Vanhoucke, and Andrew Rabinovich, “Going Deeper with Convolutions,” In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1-9, 2015.
[5] Neil J Vickers, “Animal Communication: When I'm Calling You, Will You Answer Too?,” Current Biology, vol. 27, no. 14, pp. R713R715, 2017.
[6] Hiroki Tanaka, Shih-Wei Chiu, Takanori Watanabe, Setsuko Kaoku, and Takuhiro Yamaguchi, "Computer-Aided Diagnosis System for Breast Ultrasound Images using Deep Learning,” Physics in Medicine & Biology, vol. 64, no. 23, pp. 235013, 2019.
[7] Walid Al-Dhabyani, Mohammed Gomaa, Hussien Khaled, and Fahmy Aly, “Deep Learning Approaches for Data Augmentation and Classification of Breast Masses Using Ultrasound Images,” Int. J. Adv. Comput. Sci. Appl, vol. 10, no. 5, pp. 111, 2019.
[8] Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna, “Rethinking the Inception Architecture for Computer Vision,” In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2818-2826, 2016.
[9] Barret Zoph, Vijay Vasudevan, Jonathon Shlens, and Quoc V Le, “Learning Transferable Architectures for Scalable Image Recognition,” In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 8697-8710, 2018.
[10] Ting Xiao, Lei Liu, Kai Li, Wenjian Qin, Shaode Yu, and Zhicheng Li, “Comparison of Transferred Deep Neural Networks in Ultrasonic Breast Mass Discrimination,” Biomed Research International, 2018.
[11] Heqing Zhang, Lin Han, Ke Chen, Yulan Peng, and Jiangli Lin, “Diagnostic Efficiency of the Breast Ultrasound Computer-Aided Prediction Model Based on Convolutional Neural Network in Breast Cancer,” Journal of Digital Imaging, vol. 33, pp. 1218-1223, 2020.
[12] Abdullah-Al Nahid and Yinan Kong. “Histopathological Breast-Image Classification using Local and Frequency Domains by a Convolutional Neural Network,” Information, vol. 9, no. 1, pp. 19, 2018.
[13] Abdullah-Al Nahid, Mohamad Ali Mehrabi, and Yinan Kong, “Histopathological Breast Cancer Image Classification by Deep Neural Network Techniques Guided by Local Clustering,” Biomed Research International, 2018.
[14] Mehedi Masud, Amr E Eldin Rashed, and M Shamim Hossain, “Convolutional Neural Network-Based Models for Diagnosis of Breast Cancer,” Neural Computing and Applications, pp. 1-12, 2020.
[15] Barath Narayanan Narayanan, Vignesh Krishnaraja, and Redha Ali, “Convolutional Neural Network for Classification of Histopathology Images for Breast Cancer Detection,” In 2019 IEEE National Aerospace and Electronics Conference NAECON, pp. 291-295, 2019.
[16] L. Tsochatzidis, P. Koutla, L. Costaridou, and I. Pratikakis, “Integrating Segmentation Information into CNN for Breast Cancer Diagnosis of Mammographic Masses,” Computer Methods and Programs in Biomedicine, vol. 200, no. 105913, 2021.
[17] Leung, J., Martin, J. and McLaughlin, D, “Rural-Urban Disparities in the Stage of Breast Cancer at Diagnosis in Australian women,” The Australian Journal of Rural Health, 2016. DOI: 10.1111/ajr.12271.
[18] Khan, S., Islam, N., Jan, Z., Din, I. U., & Rodrigues, J. J. C, “A Novel Deep Learning Based Framework for the Detection and Classification of Breast Cancer using Transfer Learning,” Pattern Recognition Letters, vol. 125, pp. 1-6, 2019.
[19] Ayana, G., Dese, K., & Choe, S. W, “Transfer Learning in Breast Cancer Diagnoses via Ultrasound Imaging,” Cancers, vol. 13, no. 4, pp. 738, 2021.
[20] Vesal, S., Ravikumar, N., Davari, A., Ellmann, S., & Maier, A, “Classification of Breast Cancer Histology Images using Transfer Learning,” In International Conference Image Analysis and Recognition, pp. 812-819, 2018.
[21] Chang, J., Yu, J., Han, T., Chang, H. J., & Park, E, “A Method for Classifying Medical Images using Transfer Learning: A Pilot Study on Histopathology of Breast Cancer,” In 2017 IEEE 19th International Conference on e-Health Networking, Applications and Services, Healthcom, pp. 1-4, 2017.
[22] Saber, A., Sakr, M., Abo-Seida, O. M., Keshk, A., & Chen, H. “A Novel Deep-Learning Model for Automatic Detection and Classification of Breast Cancer Using the Transfer-Learning Technique,” IEEE Access, vol. 9, pp. 71194-71209, 2021.
[23] Khuriwal, N., & Mishra N, “Breast Cancer Detection from Histopathological Images Using Deep Learning,” In 2018 3rd International Conference and Workshops on Recent Advances and Innovations in Engineering, ICRAIE, pp. 1-4, 2018.
[24] Makhtar, M., Rosly, R., Awang, M. K., Mohamad, M., & Zakaria, A. H., “A Multi-Classifier Method based Deep Learning Approach for Breast Cancer,” Int. J. Eng. Trends Technol, vol. 1, pp. 102-107, 2020.
[25] Purwanti E, & Apsari R, “Classification of Digital Mammograms Using Nearest Neighbor Techniques.”
[26] Kanmani P, Marikkannu P & Brindha M, “A Medical Image Classification using Id3 Classifier.”
[27] Charan, S., Khan, M. J., & Khurshid, K, “Breast Cancer Detection in Mammograms Using Convolutional Neural Network,” In 2018 International Conference on Computing, Mathematics and Engineering Technologies (iCoMET), pp. 1-5, 2018.
[28] Kwon S., Lee H., & Lee S, “Image Enhancement with Gaussian Filtering in the Time-Domain Microwave Imaging System for Breast Cancer Detection,” Electronics Letters, vol. 52, no. 5, pp. 342-344, 2016.
[29] Kaur M., Kaur J. and Kaur J, “Survey of Contrast Enhancement Techniques based on Histogram Equalization,” IJASA, vol. 2, no. 7, pp. 137-141, 2011.
[30] Ritika, Kaur S, “Contrast Enhancement Techniques for Images a Visual Analysis,” International Journal of Computer Applications, pp. 64, no. 17, 2013.
[31] M. Loey, A. El-Sawy, and H. El-Bakry, “Deep Learning Autoencoder Approach for Handwritten Arabic DigitsRecognition,” arXiv preprint arXiv:1706.06720, 2017
[32] Feng Y, Zhang, L, & Mo J, “Deep Manifold Preserving Autoencoder for Classifying Breast Cancer Histopathological Images,” IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 17, no. 1, pp. 91-101, 2018.
[33] Chen G, Xie X, & Li S, “Research on Complex Classification Algorithm of Breast Cancer Chip Based on SVM-RFE Gene Feature Screening,” Complexity, 2020.
[34] Abdulkareem S. A, & Abdulkareem Z. O, “An Evaluation of the Wisconsin Breast Cancer Dataset Using Ensemble Classifiers and RFE Feature Selection,” Int. J. Sci., Basic Appl. Res., vol. 55, no. 2, pp. 67-80, 2021.
[35] V Nair and G. E. Hinton, “Rectified Linear Units Improve Restricted Boltzmann Machines,” in Proceedings of the 27th International Conference on Machine Learning ICML-10, pp. 807-814, 2010.
[36] N Srivastava, G. E. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: A Simple Way to Prevent Neural Networks from Overfitting,” J. Mach. Learn. Res., vol. 15, no. 1, pp. 1929-1958, 2014.
[37] Wahab, N., Khan, A., & Lee, Y. S, “Transfer Learning Based Deep CNN for Segmentation and Detection of Mitoses in Breast Cancer Histopathological Images,” Microscopy, vol. 68, no. 3, pp. 216-233, 2019.