Journal of Electrical Engineering and Automation
IRO Journals
Volume 2 — Issue 4 — December 2020

Smart Digital Mammographic Screening System for Bulk Image Processing

https://doi.org/10.36548/jeea.2020.4.003
M. Duraipandian
Head of Department, Department of Computer Science and Engineering, Vivekanandha College of Technology for Women, Namakal, India
R. Vinothkanna
Department of ECE, Vivekanandha College of Technology for Women, Namakal, India
PDF
PDF

How to Cite

Duraipandian, M., and R. Vinothkanna. 2021. “Smart Digital Mammographic Screening System for Bulk Image Processing”. Journal of Electrical Engineering and Automation 2 (4): 156-61. https://doi.org/10.36548/jeea.2020.4.003.
ACM ACS APA ABNT Chicago Harvard IEEE MLA Turabian Vancouver AMA

Download Citation

Endnote/Zotero/Mendeley (RIS) BibTeX

Keywords

— Mammography
— image processing
— Bayesian classifier
— extreme learning machine
— watershed segmentation
Published: 22-02-2021

Abstract

Treating breast cancer is easier at early stages. However, proper diagnosis is essential for this purpose. Mammography helps in early detection of cancer cells. Existence of masses, calcification and mammogram are the evidences that help radiologists in early cancer identification. This paper proposes a smart digital mammographic screening system for processing images in large volumes irrespective of the nature of images. Watershed segmentation is performed based on appropriate selection of internal and external markers using multiple threshold extended maxima transformations in this technique. Distinguishing between healthy breast tissue and masses can be performed efficiently using a two-stage classifier. Extreme Learning Machine based single layer feed forward network along with Bayesian classifier is used for reducing false positive areas. Feature vector with features like texture and contrast are calculated using these approaches. Digital Mammography Screening database (DMS) is created with 100 mammographic images for the purpose of evaluation. Further, online databases like Breast Cancer Database (BCDB) and BreakHis are also used for analysis. Overall sensitivity of the datasets using the Bayesian classifier and Extreme Learning Machine is found to be 85% and 90% respectively.

References

  1. Al-antari, M. A., Al-masni, M. A., Park, S. U., Park, J., Metwally, M. K., Kadah, Y. M., ... & Kim, T. S. (2018). An automatic computer-aided diagnosis system for breast cancer in digital mammograms via deep belief network. Journal of Medical and Biological Engineering, 38(3), 443-456.
  2. Kim, E. K., Kim, H. E., Han, K., Kang, B. J., Sohn, Y. M., Woo, O. H., & Lee, C. W. (2018). Applying data-driven imaging biomarker in mammography for breast cancer screening: preliminary study. Scientific reports, 8(1), 1-8.
  3. Shen, L., Margolies, L. R., Rothstein, J. H., Fluder, E., McBride, R., & Sieh, W. (2019). Deep learning to improve breast cancer detection on screening mammography. Scientific reports, 9(1), 1-12.
  4. Salvi, M., Acharya, U. R., Molinari, F., & Meiburger, K. M. (2020). The impact of pre-and post-image processing techniques on deep learning frameworks: A comprehensive review for digital pathology image analysis. Computers in Biology and Medicine, 104129.
  5. Aboutalib, S. S., Mohamed, A. A., Berg, W. A., Zuley, M. L., Sumkin, J. H., & Wu, S. (2018). Deep learning to distinguish recalled but benign mammography images in breast cancer screening. Clinical Cancer Research, 24(23), 5902-5909.
  6. Xie, L., Zhang, L., Hu, T., Huang, H., & Yi, Z. (2020). Neural networks model based on an automated multi-scale method for mammogram classification. Knowledge-Based Systems, 208, 106465.
  7. Ahmed, L., Iqbal, M. M., Aldabbas, H., Khalid, S., Saleem, Y., & Saeed, S. (2020). Images data practices for semantic segmentation of breast cancer using deep neural network. Journal of Ambient Intelligence and Humanized Computing, 1-17.
  8. Shi, P., Wu, C., Zhong, J., & Wang, H. (2019, August). Deep Learning from Small Dataset for BI-RADS Density Classification of Mammography Images. In 2019 10th International Conference on Information Technology in Medicine and Education (ITME) (pp. 102-109). IEEE.
  9. Tan, Y. J., Sim, K. S., & Ting, F. F. (2017, November). Breast cancer detection using convolutional neural networks for mammogram imaging system. In 2017 International Conference on Robotics, Automation and Sciences (ICORAS) (pp. 1-5). IEEE.
  10. Thakur, R. S., Yadav, R. N., & Gupta, L. (2019). State-of-art analysis of image denoising methods using convolutional neural networks. IET Image Processing, 13(13), 2367-2380.
  11. Vijayakumar, T. (2019). Neural network analysis for tumor investigation and cancer prediction. Journal of Electronics, 1(02), 89-98.
  12. Pandian, A. P. (2019). Identification and classification of cancer cells using capsule network with pathological images. Journal of Artificial Intelligence, 1(01), 37-44.
  13. Shakya, S. (2020). Analysis of artificial intelligence based image classification techniques. Journal of Innovative Image Processing (JIIP), 2(01), 44-54.
  14. Shakya, S. (2020). Analysis of artificial intelligence based image classification techniques. Journal of Innovative Image Processing (JIIP), 2(01), 44-54.