Mammogram Classification Using Support Vector Machine

Author:

Ben Youssef Youssef1,Abdelmounim Elhassane1,Belaguid Abdelaziz1

Affiliation:

1. Hassan 1st University, Morocco

Abstract

Among the objectives of artificial intelligence techniques, we find computer-aided diagnosis systems that support preventive medical check-ups and perform detection, recognition, and classification patterns. Recently these techniques are emerged in different areas particularly in medical imaging. Medical image is an important source of information, and a golden tool for the diagnosis and assessment of a pathological analysis process. In this chapter Computer-Aided Diagnosis (CAD) system is proposed in detection and diagnosis of breast cancer, it is mainly composed of the following steps: preprocessing mammographic image, segmentation of suspect region on the mammographic image using Chan Vese model, extraction of global and local descriptors and then image classification into malignant and benign mammograms using Support Vector Machine (SVM) classifier. The analysis of mammographic images proposed system with a choice of the subset of local descriptors after tumor segmentation leads to a classification of malignant and benign mammograms. System proposed achieves 92% for accuracy.

Publisher

IGI Global

Reference109 articles.

1. Support Vector Machines for Pattern Classification

2. Amadou, B. H. (2006). Classification Dynamique de Données non-stationnaires: Apprentissage Séquentiel des Classes évolutives. (Thèse de Doctorat). Université des Sciences et Technologies de Lille, France.

3. American College of Radiology. (1994). Breast imaging reporting and data system (3rd ed.). Reston, VA: American College of Radiology.

4. American College of Radiology. (2003). ACR BI-RADS-mammography, ultrasound and magnetic resonance imaging (4th ed.). Reston, VA: American College of Radiology.

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