Classification of mammogram for early detection of breast cancer using SVM classifier and Hough transform. (November 2019)

• Record Type: Journal Article
• Authors: Vijayarajeswari, R.; Parthasarathy, P.; Vivekanandan, S.; Basha, A. Alavudeen
• Is Part Of: Measurement. Volume 146(2019)
• Page Start: 800
• Page End: 805
• Publication Date: 2019-11
• Subjects: Support vector machine -- Hough transform -- Formatting -- Styling -- Breast cancer; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2019.05.083
• Languages: English
• ISSNs: 0263-2241

Abstract

Highlights: Early prediction for mammography. Classification of mammograms using feature extracted using Hough transform. Strategies for classification and feature extraction. The classification accuracy is more by the use of SVM classifier. Proposed method is effectively classify the abnormal classes of mammograms. Abstract: Breast cancer is one of the significant health problems in the world. If these abnormalities in breast cancer are detected early there is a maximum chance for recovery. For this early prediction we can go for mammography. It is one of the most effective and commonly used method for detecting and screening breast cancer. This paper presents classification of mammograms using feature extracted using Hough transform. Hough transform is a two dimensional transform. It is used to isolate feature of particular shape in an image. Miniaturized scale characterization and masses are the two most vital markers of threat, and their mechanized identification is exceptionally important for early breast cancer diagnosis. Since masses are regularly undefined from the encompassing parenchymal, computerized mass location and arrangement is significantly additionally difficult. This paper talks about the strategies for classification and feature extraction. Here, Hough transform is used to detect features of mammograms image and it is classified using SVM. The classification accuracy is more by the use of SVM classifier. This method is tested on 95 mammograms images collected and classified using SVM. From the result it shows that the proposed method is effectively classify the abnormal classes of mammograms.