Multiresolution-Based Singular Value Decomposition Approach for Breast Cancer Image Classification.
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 rema...
| Publicado en: | BioMed Research International pp. 1 - 12 |
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| Autores principales: | , , , , , , |
| Formato: | algorithm diagnostic images equations & formulas tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
8/11/2022
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=158479555&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 158479555 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 8/11/2022 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 158479555 158479555 158479555 10.1155/2022/6392206 158479555 ppf: 1 ppct: 11 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Multiresolution-Based Singular Value Decomposition Approach for Breast Cancer Image Classification. aug: au: Mann, Suman Bindal, Amit Kumar Balyan, Archana Shukla, Vijay Gupta, Zatin Tomar, Vivek Miah, Shahajan affil: Department of Information Technology, Maharaja Surajmal Institute of Technology, New Delhi, India sug: subj: Breast Neoplasms Classification Mammography Methods Algorithms Sensitivity and Specificity Spectrum Analysis Support Vector Machine ab: 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. pubtype: Academic Journal doctype: algorithm diagnostic images equations & formulas tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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