ArticleJournal of digital imaging2015
Computer-aided diagnosis of malignant mammograms using Zernike moments and SVM.
Article in Journal of digital imaging, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Multidimensional data analysis and classification using SMIAL.Communications biology · 2026Article
- 3D dendritic spines shape descriptors for efficient classification and morphology analysis in control and Alzheimer's disease modeling neurons.Bioinformatics (Oxford, England) · 2026Article
- Application of CAD Systems in Breast Cancer Diagnosis Using Machine Learning Techniques: An Overview of Systematic Reviews.Bioengineering (Basel, Switzerland) · 2025Review
- Review
- Machine-Learning-Based Disease Diagnosis: A Comprehensive Review.Healthcare (Basel, Switzerland) · 2022Review
- Breast Cancer Mammograms Classification Using Deep Neural Network and Entropy-Controlled Whale Optimization Algorithm.Diagnostics (Basel, Switzerland) · 2022Article
- Comparative analysis of proficiencies of various textures and geometric features in breast mass classification using k-nearest neighbor.Visual computing for industry, biomedicine, and art · 2022Article
- Identification of Anomalies in Mammograms through Internet of Medical Things (IoMT) Diagnosis System.Computational intelligence and neuroscience · 2022Article
- A Bottom-Up Review of Image Analysis Methods for Suspicious Region Detection in Mammograms.Journal of imaging · 2021Review
- Improving the Prediction of Benign or Malignant Breast Masses Using a Combination of Image Biomarkers and Clinical Parameters.Frontiers in oncology · 2021Article
- A Hybridized ELM for Automatic Micro Calcification Detection in Mammogram Images Based on Multi-Scale Features.Journal of medical systems · 2019Article
- Microcalcification Segmentation from Mammograms: A Morphological Approach.Journal of digital imaging · 2017Article
- Feature Extraction and Classification on Esophageal X-Ray Images of Xinjiang Kazak Nationality.Journal of healthcare engineering · 2017Article
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2 authors.
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Abstract
This work is directed toward the development of a computer-aided diagnosis (CAD) system to detect abnormalities or suspicious areas in digital mammograms and classify them as malignant or nonmalignant. Original mammogram is preprocessed to separate the breast region from its background. To work on the suspicious area of the breast, region of interest (ROI) patches of a fixed size of 128×128 are extracted from the original large-sized digital mammograms. For training, patches are extracted manually from a preprocessed mammogram. For testing, patches are extracted from a highly dense area identified by clustering technique. For all extracted patches corresponding to a mammogram, Zernike moments of different orders are computed and stored as a feature vector. A support vector machine (SVM) is used to classify extracted ROI patches. The experimental study shows that the use of Zernike moments with order 20 and SVM classifier gives better results among other studies. The proposed system is tested on Image Retrieval In Medical Application (IRMA) reference dataset and Digital Database for Screening Mammography (DDSM) mammogram database. On IRMA reference dataset, it attains 99% sensitivity and 99% specificity, and on DDSM mammogram database, it obtained 97% sensitivity and 96% specificity. To verify the applicability of Zernike moments as a fitting texture descriptor, the performance of the proposed CAD system is compared with the other well-known texture descriptors namely gray-level co-occurrence matrix (GLCM) and discrete cosine transform (DCT).
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