Volume : IX, Issue : XII, December - 2019

Research on different feature extraction and mammogram classification techniques

Anita Kaklotar

Abstract :

Breast cancer is the primary and the most common disease found among women. Today, mammography is the most powerful screening technique used for early detection of cancer which increases the chance of successful treatment. In order to correctly detect the mammogram images as being cancerous or malignant, there is a need of a classifier. With this objective, an attempt is made to analyze different feature extraction techniques and classifiers. In the proposed system we first do the preprocessing of the mammogram images, where the unwanted noise and disturbances in the mammograms are removed. Features are then extracted from the mammogram images using Gray Level Co–Occurrences Matrix (GLCM) and Scale Invariant Feature Transform (SIFT). Finally, the features are classified using classifiers like HiCARe (Classifier based on High Confidence Association Rule Agreements), Support Vector Machine (SVM), Naïve Bayes classifier and K–NN Classifier. Further we test the images and classify them as benign or malignant class.

Keywords :

Article: Download PDF    DOI : 10.36106/ijar/2612679  

Cite This Article:

RESEARCH ON DIFFERENT FEATURE EXTRACTION AND MAMMOGRAM CLASSIFICATION TECHNIQUES, Anita Kaklotar INDIAN JOURNAL OF APPLIED RESEARCH : Volume-9 | Issue-12 | December-2019

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