skip to main content
10.1145/2851613.2851825acmconferencesArticle/Chapter ViewAbstractPublication PagessacConference Proceedingsconference-collections
research-article

Optimized multilayer perceptron using dynamic learning rate based microwave tomography breast cancer screening

Published: 04 April 2016 Publication History

Abstract

A performance of classification tool in a Computer Aided Diagnosis (CAD) software directly affects capacity of entire breast cancer screening. Most developed classification tools have mainly focused on standard techniques, for example, Magnetic Resonance Imaging (MRI), x-ray mammography, and ultrasound. With the advent of new technology, Microwave Tomography Imaging (MTI), it was inevitable to develop a desirable classification tool showing compromised performance. Yet, existing classification model using Artificial Neural Network (ANN) for handling data from the MTI shows non-negligible optimization scheme. In this paper, we present an improved model, Multilayer Perceptron (MLP) using Dynamic Learning Rate (DLR) in order to obtain better performance with optimized setting for binary classification that can be plugged into the CAD software platform. The proposed model has an optimized size of neural network so that it will not fall into indeterminate equation problem by having reasonable amount of weights between each perceptron Also, the proposed model will dynamically assign a learning rate onto each training points in the way that model earmarks a higher learning rate onto each training points belonging into minority class in order to escape from local minima which is a typical jeopardy of ANN. In experiment, we evaluated performance with following measures; precision, recall, specificity, accuracy, and Matthews Correlation Coefficient (MCC). Experimental result shows that MLP using DLR outperforms overall measures over existing ANN dealt with MTI.

References

[1]
Aminikhanghahi, S., Wang, W., Shin, S., Son, S. H., and Jeon, S. I., 2014. Effective tumor feature extraction for smart phone based microwave tomography breast cancer screening. In Proceedings of the 29th Annual ACM Symposium on Applied Computing ACM, 674--679.
[2]
Anderson, D. Z., 1988. Neural information processing systems: Denver, Colo., 1987. Springer Science & Business Media.
[3]
Baker, J. A., Rosen, E. L., Lo, J. Y., Gimenez, E. I., Walsh, R., and SOO, M. S., 2003. Computer-aided detection (CAD) in screening mammography: sensitivity of commercial CAD systems for detecting architectural distortion. American Journal of Roentgenology 181, 4, 1083--1088.
[4]
Fear, E. C., Meaney, P. M., and Stuchly, M., 2003. Microwaves for breast cancer detection? Potentials, IEEE 22, 1, 12--18.
[5]
Floyd, C. E., Lo, J. Y., Yun, A. J., Sullivan, D. C., and Kornguth, P. J., 1994. Prediction of breast cancer malignancy using an artificial neural network. Cancer 74, 11, 2944--2948.
[6]
Lecun, Y. A., Bottou, L., Orr, G. B., and Müller, K.-R., 2012. Efficient backprop. In Neural networks: Tricks of the trade Springer, 9--48.
[7]
Liu, H., Li, J., and Wong, L., 2002. A comparative study on feature selection and classification methods using gene expression profiles and proteomic patterns. Genome informatics 13, 51--60.
[8]
Noghanian, S., 2012. Microwave Tomography for Biomedical Quantitative Imaging. J Elec Electron 1, e107.
[9]
Santorelli, A., Porter, E., Kirshin, E., Liu, Y. J., and Popovic, M., 2014. Investigation of classifiers for tumor detection with an experimental time-domain breast screening system. Progress In Electromagnetics Research 144, 45--57.
[10]
Sharma, S. and Khanna, P., 2015. Computer-Aided Diagnosis of Malignant Mammograms using Zernike Moments and SVM. Journal of digital imaging 28, 1, 77--90.
[11]
Wu, Y., Giger, M. L., Doi, K., Vyborny, C. J., Schmidt, R. A., and Metz, C. E., 1993. Artificial neural networks in mammography: application to decision making in the diagnosis of breast cancer. Radiology 187, 1, 81--87.
[12]
Yu, X.-H., Chen, G.-A., and Cheng, S.-X., 1995. Dynamic learning rate optimization of the backpropagation algorithm. Neural Networks, IEEE Transactions on 6, 3, 669--677.
[13]
Zhang, W., Doi, K., Giger, M. L., Wu, Y., Nishikawa, R. M., and Schmidt, R. A., 1994. Computerized detection of clustered microcalcifications in digital mammograms using a shift-invariant artificial neural network. Medical Physics 21, 4, 517--524.

Cited By

View all
  • (2024)Computational Techniques in PET/CT Image Processing for Breast Cancer: A Systematic Mapping ReviewACM Computing Surveys10.1145/364835956:8(1-38)Online publication date: 26-Apr-2024
  • (2019)Deep learning-based breast cancer classification through medical imaging modalities: state of the art and research challengesArtificial Intelligence Review10.1007/s10462-019-09716-553:3(1655-1720)Online publication date: 25-May-2019

Index Terms

  1. Optimized multilayer perceptron using dynamic learning rate based microwave tomography breast cancer screening

      Recommendations

      Comments

      Information & Contributors

      Information

      Published In

      cover image ACM Conferences
      SAC '16: Proceedings of the 31st Annual ACM Symposium on Applied Computing
      April 2016
      2360 pages
      ISBN:9781450337397
      DOI:10.1145/2851613
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

      Sponsors

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      Published: 04 April 2016

      Permissions

      Request permissions for this article.

      Check for updates

      Author Tags

      1. artificial neural network
      2. classification
      3. computer aided diagnosis system
      4. microwave tomography
      5. performance

      Qualifiers

      • Research-article

      Funding Sources

      Conference

      SAC 2016
      Sponsor:
      SAC 2016: Symposium on Applied Computing
      April 4 - 8, 2016
      Pisa, Italy

      Acceptance Rates

      SAC '16 Paper Acceptance Rate 252 of 1,047 submissions, 24%;
      Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

      Upcoming Conference

      SAC '25
      The 40th ACM/SIGAPP Symposium on Applied Computing
      March 31 - April 4, 2025
      Catania , Italy

      Contributors

      Other Metrics

      Bibliometrics & Citations

      Bibliometrics

      Article Metrics

      • Downloads (Last 12 months)2
      • Downloads (Last 6 weeks)1
      Reflects downloads up to 20 Jan 2025

      Other Metrics

      Citations

      Cited By

      View all
      • (2024)Computational Techniques in PET/CT Image Processing for Breast Cancer: A Systematic Mapping ReviewACM Computing Surveys10.1145/364835956:8(1-38)Online publication date: 26-Apr-2024
      • (2019)Deep learning-based breast cancer classification through medical imaging modalities: state of the art and research challengesArtificial Intelligence Review10.1007/s10462-019-09716-553:3(1655-1720)Online publication date: 25-May-2019

      View Options

      Login options

      View options

      PDF

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader

      Media

      Figures

      Other

      Tables

      Share

      Share

      Share this Publication link

      Share on social media