ICTACT Journal on Soft Computing

Open Access Open Access  Restricted Access Subscription Access

An Automatic Detection of Breast Cancer using Efficient Feature Extraction and Optimized Classifier Model


Affiliations
1 Department of Computer Applications, Government College for Women, Srikakulam, India
2 Department of Computer Science, Sri Padmavati Mahila Visvavidyalayam, India
 

   Subscribe/Renew Journal


Image Processing Techniques (IPTs) are widely used in healthcare. IPTs work on images to extract useful information from them. IPTs are effective in the early diagnosis of cancers which helps in their proper treatment. The number of human beings affected by cancer has drastically increased recently and more in women in the form of Breast Cancers (BCs). This work is on detecting BCs from image datasets using IPTs. This work proposes the use of Weiner Filters (WFs) in Preprocessing BC image data. WFs are applied to images as they can improve the quality of images. Image segmentations are executed using the Watershed Algorithm which detects Regions of Interest (ROIs) in images. Feature Extractions are done using the statistical method Gray Level Co-occurrence Matrix (GLCM). These extracted features are then selected using Black Widow Optimization (BWO) Algorithm. The selected features are then trained by Fuzzy Neural Networks (FNNs) which classify BC cases. The results of the classifier when evaluated were found to have a better performance on medical images in terms of Accuracy.

Keywords

Breast Cancer Detection, Weiner Filter, Feature Extraction, GLCM, Feature Selection, Black Widow Optimization (BWO) Algorithm, Classification, Fuzzy Neural Network (FNN)
Subscription Login to verify subscription
User
Notifications
Font Size

  • M.M. Islam, M.R. Haque and M.N. Kabir, “Breast Cancer Prediction: A Comparative Study using Machine Learning Techniques”, SN Computer Science, Vol. 1, No. 5, pp. 1-14, 2020.

  • P.H. Abreu, M.S. Santos, M.H. Abreu and D.C. Silva, “Predicting Breast Cancer Recurrence using Machine Learning Techniques: A Systematic Review”, ACM Computing Surveys, Vol. 49, No. 3, pp. 1-40, 2016.

  • J.A. Cruz and D.S. Wishart, “Applications of Machine Learning in Cancer Prediction and Prognosis”, Cancer Informatics, Vol. 2, pp. 1-13, 2006.

  • B.M. Gayathri, C.P. Sumathi and T. Santhanam, “Breast Cancer Diagnosis using Machine Learning Algorithms-A Survey”, International Journal of Distributed and Parallel Systems, Vol. 4, No. 3, pp. 105-114, 2013.

  • S. Sharma and T. Choudhury, “Breast Cancer Detection using Machine Learning Algorithms”, Proceedings of International Conference on Computational Techniques, Electronics and Mechanical Systems, pp. 114-118, 2018.

  • P. Patro, K. Kumar and G. Suresh Kumar, Similarity and Wavelet Transform based Data Partitioning and Parameter Learning for Fuzzy Neural Network”, Journal of King Saud University-Computer and Information Sciences, Vol. 78, No. 3, pp. 1-17, 2020.

  • K. Anastraj, T. Chakravarthy and T. Poondi, “Breast Cancer Detection Either Benign or Malignant Tumor using Deep Convolutional Neural Network with Machine Learning Techniques”, Proceedings of International Conference on Computational Techniques, Electronics and Mechanical Systems, pp. 566-573, 2018.

  • H.S. Hota, “Diagnosis of Breast Cancer using Intelligent Techniques”, International Journal of Emerging Science and Engineering, Vol. 1, No. 3, pp. 45-53, 2013.

  • S. Singh, S. Saini and M. Singh, “Cancer Detection using Adaptive Neural Network”, International Journal of Advancements in Research and Technology, Vol. 1, No. 4, pp. 1-5, 2012.

  • P. Patro, K. Kumar and G. Suresh Kumar, “Neuro Fuzzy System with Hybrid Ant Colony Particle Swarm Optimization (HASO) and Robust Activation”, Journal of Advanced Research in Dynamical and Control Systems, Vol. 12, No. 3, pp. 741-750, 2020.

  • T. Eklund, M. Collan, P. Jalava and Y. Collan, “On Visual Exploration of Breast Cancer Data using the Self-Organizing Map”, Proceedings of International Workshop on Self-Organizing Maps, pp. 139-146, 2005.

  • M.M. Beg and M. Jain, “An Analysis of the Methods Employed for Breast Cancer Diagnosis”, Proceedings of International Conference on Machine Learning, pp. 1-13, 2012.

  • A. Raad, A. Kalakech and M. Ayache, “Breast Cancer Classification using Neural Network Approach: MLP and RBF”, Wireless Networks, Vol. 7, No. 8, pp. 1-9, 2012.

  • P. Patro, K. Kumar and G. Suresh Kumar, “Optimized Hybridization of Ant Colony Optimization and Genetic Algorithm (HACOGA) Based IC-FNN Classifier for Abalone”, Journal of Computational and Theoretical Nanoscience, Vol. 17, pp.2756-2764, 2020.

  • J. Tyrer, S.W. Duffy and J. Cuzick, “A Breast Cancer Prediction Model Incorporating Familial and Personal Risk Factors”, Statistics in Medicine, Vol. 23, No. 7, pp. 1111-1130, 2004.

  • J.A. Baker and C.E. Floyd, “Breast Cancer: Prediction with Artificial Neural Network based on BI-RADS Standardized Lexicon”, Radiology, Vol. 196, No. 3, pp. 817-822, 1995.

  • M.W. Huang, C.W. Chen and C.F. Tsai, “SVM and SVM Ensembles in Breast Cancer Prediction”, PloS One, Vol. 12, No. 1, pp. 1-13, 2017.

  • M. Kumari and V. Singh, “Breast Cancer Prediction System”, Procedia Computer Science, Vol. 132, pp. 371-376, 2018.

  • J.S. Reis Filho and L. Pusztai, “Gene Expression Profiling in Breast Cancer: Classification, Prognostication, and Prediction”, The Lancet, Vol. 378, pp. 1812-1823, 2011.

  • C.J. Fabian, S. Zeiger and M.S. Mayo, “Short-Term Breast Cancer Prediction by Random Periareolar Fine-Needle Aspiration Cytology and the Gail Risk Model”, Journal of the National Cancer Institute, Vol. 92, No. 15, pp. 1217-1227, 2000.

  • H. Asri, H. Mousannif, H. Al Moatassime and T. Noel, “Using Machine Learning Algorithms for Breast Cancer Risk Prediction and Diagnosis”, Procedia Computer Science, Vol. 83, pp. 1064-1069, 2016.

  • M. Amrane, S. Oukid, I. Gagaoua and T. Ensari, “Breast Cancer Classification using Machine Learning”, Proceedings of International Conference on Electric Electronics, pp. 1-4, 2018.

  • S. Turgut and T. Ensari, “Microarray Breast Cancer Data Classification using Machine Learning Methods”, Proceedings of International Conference on Electric Electronics, Computer Science, Biomedical Engineering, p. 1-3, 2018.

  • P. Patro, K. Kumar and G. Suresh Kumar, “Various Classifiers Performance Based Machine Learning Methods”, International Journal of Recent Technology and Engineering, Vol. 8, No. 3, pp. 305-310, 2019.

  • S. Sharma, A. Aggarwal and T. Choudhury, “Breast Cancer Detection using Machine Learning Algorithms”, Proceedings of International Conference on Computational Techniques, Electronics and Mechanical Systems, pp. 114-118, 2018.

  • A. Alarabeyyat and M. Alhanahnah, “Breast Cancer Detection using K-Nearest Neighbor Machine Learning Algorithm”, Proceedings of International Conference on Developments in Systems Engineering, pp. 35-39. 2016.

  • R.J. Hemalatha and R. Chandrasekaran, “Active Contour based Segmentation Techniques for Medical Image Analysis”, Medical and Biological Image Analysis, Vol. 4, pp. 1-17, 2018.

  • C.R. Park, S.H. Kang and Y. Lee, “Median Modified Wiener Filter for Improving the Image Quality of Gamma Camera Images”, Nuclear Engineering and Technology, Vol. 52, No. 10, pp. 2328-2333, 2020.

  • T. Srikanth, P.P. Kumar and A. Kumar, “Color Image Segmentation using Watershed Algorithm”, International Journal of Computer Science and Information Technologies, Vol. 2, No. 5, pp. 2332-2334, 2011.

  • P. Mohanaiah, P. Sathyanarayana and L. GuruKumar, “Image Texture Feature Extraction using GLCM Approach”, International Journal of Scientific and Research Publications, Vol. 3, No. 5, pp. 1-5, 2013.

  • V. Hayyolalam and A.A.P. Kazem, “Black Widow Optimization Algorithm: A Novel Meta-Heuristic Approach for Solving Engineering Optimization Problems”, Engineering Applications of Artificial Intelligence, Vol. 87, pp. 1-15, 2020.

  • M. Velmurugan and K. Thangavel, “Mammogram Classification using Fuzzy Neural Network”, International Journal of Computational Intelligence System, Vol. 3, No. 3, pp. 1-12, 2013.


Abstract Views: 260

PDF Views: 103




  • An Automatic Detection of Breast Cancer using Efficient Feature Extraction and Optimized Classifier Model

Abstract Views: 260  |  PDF Views: 103

Authors

S. Vani Kumari
Department of Computer Applications, Government College for Women, Srikakulam, India
K. Usha Rani
Department of Computer Science, Sri Padmavati Mahila Visvavidyalayam, India

Abstract


Image Processing Techniques (IPTs) are widely used in healthcare. IPTs work on images to extract useful information from them. IPTs are effective in the early diagnosis of cancers which helps in their proper treatment. The number of human beings affected by cancer has drastically increased recently and more in women in the form of Breast Cancers (BCs). This work is on detecting BCs from image datasets using IPTs. This work proposes the use of Weiner Filters (WFs) in Preprocessing BC image data. WFs are applied to images as they can improve the quality of images. Image segmentations are executed using the Watershed Algorithm which detects Regions of Interest (ROIs) in images. Feature Extractions are done using the statistical method Gray Level Co-occurrence Matrix (GLCM). These extracted features are then selected using Black Widow Optimization (BWO) Algorithm. The selected features are then trained by Fuzzy Neural Networks (FNNs) which classify BC cases. The results of the classifier when evaluated were found to have a better performance on medical images in terms of Accuracy.

Keywords


Breast Cancer Detection, Weiner Filter, Feature Extraction, GLCM, Feature Selection, Black Widow Optimization (BWO) Algorithm, Classification, Fuzzy Neural Network (FNN)

References