Classification of mammograms for breast cancer detection using fusion of discrete cosine transform and discrete wavelet transform features.

Muhammad Talha Naseem Uppal

Abstract

Classification of mammograms for breast cancer detection using fusion of discrete cosine transform and discrete wavelet transform features.

Radiologists mainly depend upon computer aided detection/diagnosis (CAD) in order to rule out the indirect symptoms of malignant cells such as microcalcifications, architectural distortion and ill-defined masses in digital mammograms. A mammogram is low-contrast image whose quality needs to be enhanced for clarity and better interpretation. For this purpose, Genetic Programming (GP) based filter is proposed, while the fusion of Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT) features is also proposed which is used as an input to classifier. The proposed scheme accomplishes 96.97% accuracy, 98.39% sensitivity and 94.59% specificity for classifying mammograms into normal and abnormal (cancer) categories using SVM (Support Vector Machine) classifier and MIAS (Mammographic Institute Society Analysis) dataset.

Author(s): Muhammad Talha Naseem Uppal
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