Multiresolution-Based Singular Value Decomposition Approach for Breast Cancer Image Classification

• Published in: BioMed research international 2022, Vol.2022 (1), p.6392206-6392206
• Main Authors: Mann, Suman, Bindal, Amit Kumar, Balyan, Archana, Shukla, Vijay, Gupta, Zatin, Tomar, Vivek, Miah, Shahajan
• Format: Article
• Language: English
• Subjects: Adrenal glands; Algorithms; Biomedical research; Breast; Breast cancer; Breast Neoplasms - diagnostic imaging; Classification; Decomposition; Diagnosis; Feature extraction; Female; Humans; Image classification; Image processing; Machine learning; Mammography; Mammography - methods; Medical imaging; Methods; Morphology; Singular value decomposition; Spectroscopy; Support Vector Machine; Support vector machines; Tumors; Wavelet transforms
• ISSN: 2314-6133; 2314-6141
• DOI: 10.1155/2022/6392206

Breast cancer is the most prevalent form of cancer that can strike at any age; the higher the age, the greater the risk. The presence of malignant tissue has become more frequent in women. Although medical therapy has improved breast cancer diagnostic and treatment methods, still the death rate remains high due to failure of diagnosing breast cancer in its early stages. A classification approach for mammography images based on nonsubsampled contourlet transform (NSCT) is proposed in order to investigate it. The proposed method uses multiresolution NSCT decomposition to the region of interest (ROI) of mammography images and then uses Z-moments for extracting features from the NSCT-decomposed images. The matrix is formed by the components that are extracted from the region of interest and are then subjected to singular value decomposition (SVD) in order to remove the essential features that can generalize globally. The method employs a support vector machine (SVM) classification algorithm to categorize mammography pictures into normal, benign, and malignant and to identify and classify the breast lesions. The accuracy of the proposed model is 96.76 percent, and the training time is greatly decreased, as evident from the experiments performed. The paper also focuses on conducting the feature extraction experiments using morphological spectroscopy. The experiment combines 16 different algorithms with 4 classification methods for achieving exceptional accuracy and time efficiency outcomes as compared to other existing state-of-the-art approaches.