Classification of Mammograms using Various Feature Extraction Methods and Machine Learning
Apanveer kaur1, Amit Doegar2 

1Apanveer kaur, Department of Computer Science & Engineering, NITTR, Sector 26, Chandigarh, India.
2Amit Doegar, Department of Computer Science & Engineering, NITTR, Sector 26, Chandigarh – 160019, India.

Manuscript received on 23 March 2019 | Revised Manuscript received on 27 March 2019 | Manuscript published on 30 July 2019 | PP: 5401-5405 | Volume-8 Issue-2, July 2019 | Retrieval Number: B3394078219/19©BEIESP | DOI: 10.35940/ijrte.B3394.078219
Open Access | Ethics and Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (

Abstract: Breast cancer is an alarming disease which takes millions of lives every year. In 2018, it was anticipated that 627,000 women died due to breast cancer – which is around 15% of all deaths caused due to different types of cancers among women. Currently, risk factors of breast cancer cannot be avoided, and early detection is the only way of survival. Automated detection of breast cancer with the help of image processing methods and machine learning algorithms helps in giving more accurate results and less human power. In the proposed system, multiple features are extracted using HSV histogram, LBP, GLCM, 2-D DWT. Support vector machine and LIBSVM classifiers are used for the classification of mammogram images if it’s benign or malign in nature. For classification, the INbreast dataset have been used which includes 115 cases containing 410 images. The dataset is divided into benign and malign category based upon BI-RAIDS scale. According to this partition we have 243 benign images and 100 malign images present in this dataset and a feature matrix of 595 features in total is generated for balanced and unbalanced datasets respectively and fed into SVM and LIBSVM to distinguish the data. The balanced datasets on LIBSVM gave best results with 92% accuracy, 84% sensitivity, 100% specificity and 91.30% F1 score followed by SVM which gave 75% accuracy, 73.61% sensitivity, 76.66% specificity and 75.8% F1 score.
Index Terms: Breast Cancer, LBP, GLCM, DWT, LIBSVM, SVM, Mammogram.

Scope of the Article: Classification