Classification of Mammograms based on Features Extraction Techniques Using Support Vector Machine

Enas Mohammed Hussein Saeed

Abstract


Now mammography is the most reliable method for early detection of breast cancer. The main objective of this research is to design a Computer-aided detection (CAD) system to help radiologists to provide a second view to diagnose mammograms. In the proposed system the medium filter and binary image with a global threshold will be applied to remove noise and small artifacts in the pre-processing phase. Secondly, In the segmentation stage, the Hybrid Bounding Box and Region Growing (HBBRG) algorithm will be used to remove the pectoral muscles, and then a geometric method has been utilized to cut the largest possible square that can be obtained from a mammogram which represents the ROI. In the feature extraction stage, three methods use to prepare a robust feature vector that represents a suitable input to the classification process are first Order (statistical features), Local Binary Patterns (LBP), and Gray-Level Co-Occurrence Matrix (GLCM). Finally, SVM has been applied in two-level to classify mammogram images in the first level to normal or abnormal, and then the classification of abnormal once in the second level to the benign or malignant image. The system was tested on the MAIS the Mammogram image analysis Society (MIAS) database, in addition to image from the Teaching Oncology Hospital, Medical City in Baghdad, where the results showed achieving an accuracy of 95.454% for the first level and 97.260% for the second level, also, the results of applying the proposed system to the MIAS database alone were achieving an accuracy of 93.105% for the first level and 94.59 for the second level.

Keywords


Breast cancer; Classification; Cut the largest possible square; Features Extraction; Digital Mammography; Support Vector Machine(SVM);



DOI: https://doi.org/10.11591/csit.v2i3.p%25p

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ISSN: 2722-323X, e-ISSN: 2722-3221

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Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.