标题：A Novel Approach to Mammogram Classification using Spatio-Temporal and Texture Feature Extraction using Dictionary based Sparse Representation Classifier
其他标题：A Novel Approach to Mammogram Classification using Spatio-Temporal and Texture Feature Extraction using Dictionary based Sparse Representation Classifier
期刊名称：International Journal of Advanced Computer Science and Applications(IJACSA)
印刷版ISSN：2158-107X
电子版ISSN：2156-5570
出版年度：2020
卷号：11
期号：10
DOI：10.14569/IJACSA.2020.0111041
出版社：Science and Information Society (SAI)
摘要：Cancer is a chronic disease and increasing rapidly worldwide. Breast cancer is one of the most crucial cancer which affects the women health and causes death of the women. In order to predict the breast cancer, mammogram is considered as a promising technique which helps to identify the early stages of cancer. However, several schemes have been developed during last decade to overcome the performance related issues but achieving the desired performance is still challenging task. To overcome this issue, we introduce a novel and robust approach of feature extraction and classification. According to the proposed approach, first of all, we apply pre-processing stage where image binarization is applied using Niblack’s method and later Region of Interest (ROI) extraction and segmentation schemes are applied. In the next phase of work, we developed a mixed strategy of feature extraction where we consider Gray Level Co-occurrence Matrix (GLCM), Histogram of oriented Gradients (HoG) with Principal Component Analysis (PCA) for dimension reduction, Scale-invariant Feature Transform (SIFT), and non-parametric Discrete Wavelet Transform (DWT) features are extracted. Finally, we present K-Singular value decomposition (SVD) based dictionary learning scheme and applied the Sparse representation classifier (SRC) classification approach and performance is evaluated using MATLAB tool. An extensive experimental study is carried out which shows that the proposed approach achieves classification accuracy as 98.13%, Precision as 97.58%, Recall as 98.36%, and F-Score 97.95%. The performance of proposed approach is compared with the state-of-art techniques which shows that the proposed approach gives better performance.
