Mammogram Classification using Curvelet Coefficients and Gray Level Co-Occurrence Matrix for Detection of Breast Cancer
Ranjit Biswas1, Sudipta Roy2, Abhijit Biswas3

1Ranjit Biswas, Department of Information Technology, Ramkrishna Mahavidyalaya, Kailashahar, India.
2Sudipta Roy, Department of Computer Science & Engineering, Assam University, Silchar, India.
3Abhijit Biswas, Department of Computer Science & Engineering, Assam University, Silchar, India. 

Manuscript received on September 16, 2019. | Revised Manuscript received on 24 September, 2019. | Manuscript published on October 10, 2019. | PP: 4819-4824 | Volume-8 Issue-12, October 2019. | Retrieval Number: L36941081219/2019©BEIESP | DOI: 10.35940/ijitee.L3694.1081219
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Abstract: Computer-aided diagnosis system plays an important role in diagnosis and detection of breast cancer. In computer-aided diagnosis, feature extraction is one of the important steps. In this paper, we have proposed a method based on curvelet transform to classify mammogram images as normal -abnormal, benign and malignant. The feature vector is computed from the approximation coefficients. Directional energy is also calculated for all sub-bands. To select the efficient feature we used t-test and f-test methods. The selected feature is applied to Artificial Neural Network (ANN) classifier for classification. The effectiveness of the proposed method has been tested on MIAS database. The performance measures are computed with respect to normal vs. abnormal and benign vs. malignant for using approximation subband and energy feature of all curvelet coefficients. The highest classification accuracy of 95.34% is achieved for normal vs. abnormal and 80.86% is achieved for benign vs malignant class using energy feature of all curvelet coefficients.
Keywords: Mammogram, ROI, Curvelet Transform, GLCM, Artificial Neural Network (ANN).
Scope of the Article: Artificial Intelligence