Identification of Micro-calcification in Mammogram for Breast Cancer Analysis using SVM Classifier

  • Authors

    • Gaurav Makwana
    • Lalita Gupta
    2018-04-12
    https://doi.org/10.14419/ijet.v7i2.16.11411
  • Breast mammogram, micro-calcification, benign, malignant, K-mean, , GLC, SVM

  • Abstract

    Breast cancer is most common disease in women of all ages. To identify & confirm the state of tumor in breast cancer diagnosis, patients are undergo biopsy number of times to identify malignancy. Early detection of cancer can save the patient. In this paper a novel approach for automatic segmentation & classification of breast calcification is proposed. The diagnostic test technique for detection of breast condition is very costly & requires human expertise whereas proposed method can help in automatically identifying the disease by comparing the data with the standard database. In proposed method a database has been created to define various stage of breast calcification & testing images are pre-processed to resize, enhance & filtered to remove background noise. Clustering is performed by using k-means clustering algorithm. GLCM is used to extract out statistical feature like area, mean, variance, standard deviation, homogeneity, skewness etc. to classify the state of tumor. SVM classifier is used for the classification using extracted feature.

     

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  • How to Cite

    Makwana, G., & Gupta, L. (2018). Identification of Micro-calcification in Mammogram for Breast Cancer Analysis using SVM Classifier. International Journal of Engineering & Technology, 7(2.16), 29-32. https://doi.org/10.14419/ijet.v7i2.16.11411

    Received date: 2018-04-12

    Accepted date: 2018-04-12

    Published date: 2018-04-12