ICTACT Journal on Soft Computing

Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

An Observation and Categorization of Breast Cancer Utilizing Support Vector and Artificial Neural Network using Discrete Wavelet Transform


Affiliations
1 Department of Information Science and Engineering, BNM Institute of Technology, India
     

   Subscribe/Renew Journal


Digital mammogram images are generally used in medical field as a standard tool for enhancing, transmission and restoring of data. The procedure of image processing is applied to diagnose breast cancer from mammographic ROI image. The quality of mammogram pictures are very low and are sometimes influenced by X-Ray absorption properties of an anatomic parts, size as well as shape. The method of pre-processing help to enhance the raw mammogram image obtained from sensors to aid in an identification of tumors. The proposed work uses Discrete Wavelet Transform (DWT) to decompose the given gray-scale image. The textual and statistical features are being extracted from spatial domain coefficients along with frequency domain coefficients. The feature extraction method used in this work is Gray-Level Co-occurrence Matrix (GLCM). Classification of image is performed using support vector and artificial neural network as benign or malignant. The proposed method is applied on Mammographic Image Analysis Society (MIAS) database. The images of the database have to undergo training, testing and validation stages.

Keywords

Mammography, Feature Extraction, Support Vector Machine, Artificial Neural Network, Gray-Level Co-Occurrence Matrix, Mammographic Image Analysis Society, Discrete Wavelet Transform.
Subscription Login to verify subscription
User
Notifications
Font Size

  • Saritha Chakrasali, M Sumathi, R Bhaskaran and R Murugesan, “Secure Dual Watermarking in the Wavelet Domain for Teleradiology”, Proceedings of International Conference on Advances in Information and communication Technologies, pp. 41-48, 2007.

  • Ibrahima Faye and Brahim Belhaouari Sami, “Digital Mammogram Classification using Wavelet Based Feature Extraction Method”, Proceedings of International Conference on Computer and Electrical Engineering, pp. 23-29, 2009.

  • H. Girish, N. Pradeep, B. Sreepathi and K. Karibasappa, “Feature Extraction of Mammograms”, International Journal of Bio Information Research, Vol. 4, No. 1, pp. 241-244, 2012.

  • Ayoub Arafi, Youssef Safi and Rkia Fajr, “Classification of Mammographic Images using Artificial Neural Networks”, Applied Mathematics Science, Vol. 7, No. 89, pp. 4415-4423, 2013.

  • Varsha J. Gaikwad, “Detection of Breast Cancer in Mammogram using Support Vector Machine”, International Journal of Scientific Engineering and Research, Vol. 3, No. 2, pp. 1-13, 2015.

  • C.S. Roopashree and Saritha Chakrasali, “Detection of Diabetic Retinopathy using Wavelet Transform and SVM Classifier”, Indian Journal for Research in Applied Sciences and Engineering Technology, Vol. 5, No. 5, pp. 32-38, 2017.

  • S. Satish and R. Vijay, “Design and Analysis of Breast Cancer Detection System Using Mammogram Features Extraction”, International Journal of Electronics and Computer Science Engineering, Vol. 3, No. 4, pp. 406-408, 2014.

  • Varsha J. Gaikwad, “Detection of Breast Cancer in Mammogram using Support Vector Machine”, International Journal of Scientific Engineering and Research, Vol. 3, No. 2, pp. 1-7, 2015.

  • Sonal Naranje, “Early Detection of Breast Cancer using ANN”, International Journal of Innovative Research in Computer and Communication Engineering, Vol. 4, No. 7, pp. 14008-14013, 2016.

  • Ranjit Biswas, Abhijith Nath and Sudiptha Roy, “Mammogram Classification Using Gray-Level Co-occurrence Matrix for Diagnosis of Breast Cancer”, Proceedings of International Conference on Micro-Electronics and Telecommunication Engineering, pp. 1-6, 2016.

  • Muhammad Talha, “Classification of Mammograms for Breast Cancer Detection using Fusion of Discrete Cosine Transform and Discrete Wavelet Transform Features”, Biomedical Research, Vol. 27, No. 2, pp. 322-327, 2016.

  • P Samyuktha and D Sriharsha “Classification of Mammograms using Gray-level Co-occurrence Matrix and Support Vector Machine Classifier”, International Journal of Engineering Trends and Technology, Vol. 3, No. 1, pp 131-137, 2017.

  • Deepthi Sehrawat, Abhishek Sherawat, Dhirendra Jaiswal and Anima Sen, “Detection and Classification of Tumor in Mammograms using Discrete Wavelet Transform and Support Vector Machine”, International Research Journal of Engineering and Technology, Vol. 4, No. 5, pp. 1328-13334, 2017.

  • C.S. Roopashree and Saritha Chakrasali. “Detection of Diabetic Retinopathy using Wavelet Transform and SVM Classifier”, Indian Journal for Research in Applied Sciences and Engineering Technology, Vol. 5, No. 5, pp. 121-127, 2017.

  • M.M. Mehdy et al., “Artificial Neural Networks in Image Processing for Early Detection of Breast Cancer”, Computational and Mathematical Methods in Medicine, Vol. 2017, pp. 1-15, 2017.

  • Nadeem Tariq, “Breast Cancer Detection using Artificial Neural Network”, Journal of Molecular Biomarkers and Diagnosis, Vol. 9, No. 1, pp. 1-6, 2017.


Abstract Views: 215

PDF Views: 0




  • An Observation and Categorization of Breast Cancer Utilizing Support Vector and Artificial Neural Network using Discrete Wavelet Transform

Abstract Views: 215  |  PDF Views: 0

Authors

Soumya Hundekar
Department of Information Science and Engineering, BNM Institute of Technology, India
Saritha Chakrasali
Department of Information Science and Engineering, BNM Institute of Technology, India

Abstract


Digital mammogram images are generally used in medical field as a standard tool for enhancing, transmission and restoring of data. The procedure of image processing is applied to diagnose breast cancer from mammographic ROI image. The quality of mammogram pictures are very low and are sometimes influenced by X-Ray absorption properties of an anatomic parts, size as well as shape. The method of pre-processing help to enhance the raw mammogram image obtained from sensors to aid in an identification of tumors. The proposed work uses Discrete Wavelet Transform (DWT) to decompose the given gray-scale image. The textual and statistical features are being extracted from spatial domain coefficients along with frequency domain coefficients. The feature extraction method used in this work is Gray-Level Co-occurrence Matrix (GLCM). Classification of image is performed using support vector and artificial neural network as benign or malignant. The proposed method is applied on Mammographic Image Analysis Society (MIAS) database. The images of the database have to undergo training, testing and validation stages.

Keywords


Mammography, Feature Extraction, Support Vector Machine, Artificial Neural Network, Gray-Level Co-Occurrence Matrix, Mammographic Image Analysis Society, Discrete Wavelet Transform.

References