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...

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Bibliographic Details
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
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Summary: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.
ISSN:2314-6133
2314-6141
DOI:10.1155/2022/6392206