ArticleBiomedical engineering online2017
Breast masses in mammography classification with local contour features.
Article in Biomedical engineering online, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- MobNas ensembled model for breast cancer prediction.Scientific reports · 2025Article
- A Short Breast Imaging Reporting and Data System-Based Description for Classification of Breast Mass Grade.Life (Basel, Switzerland) · 2024Article
- Deep learning empowered breast cancer diagnosis: Advancements in detection and classification.PloS one · 2024Article
- An efficient transfer learning based cross model classification (TLBCM) technique for the prediction of breast cancer.PeerJ. Computer science · 2023Article
- Classification of breast cancer using a manta-ray foraging optimized transfer learning framework.PeerJ. Computer science · 2022Article
- Focus on the Predictive Value of Subclassification of Extratumoral Structural Abnormalities for Malignant Nonspiculate and Noncalcified Masses on Digital Mammography.Frontiers in genetics · 2022Article
- Evaluation of the peritumoral features using radiomics and deep learning technology in non-spiculated and noncalcified masses of the breast on mammography.Frontiers in oncology · 2022Article
- A Bottom-Up Review of Image Analysis Methods for Suspicious Region Detection in Mammograms.Journal of imaging · 2021Review
- Electrical Impedance Tomography - Recent Applications and Developments.Journal of electrical bioimpedance · 2021Article
- Establishment and Verification of a Bagged-Trees-Based Model for Prediction of Sentinel Lymph Node Metastasis for Early Breast Cancer Patients.Frontiers in oncology · 2019Article
- Involvement of Machine Learning for Breast Cancer Image Classification: A Survey.Computational and mathematical methods in medicine · 2017Review
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMammography is one of the most popular tools for early detection of breast cancer. Contour of breast mass in mammography is very important information to distinguish benign and malignant mass. Contour of benign mass is smooth and round or oval, while malignant mass has irregular shape and spiculated contour. Several studies have shown that 1D signature translated from 2D contour can describe the contour features well.
methodsIn this paper, we propose a new method to translate 2D contour of breast mass in mammography into 1D signature. The method can describe not only the contour features but also the regularity of breast mass. Then we segment the whole 1D signature into different subsections. We extract four local features including a new contour descriptor from the subsections. The new contour descriptor is root mean square (RMS) slope. It can describe the roughness of the contour. KNN, SVM and ANN classifier are used to classify benign breast mass and malignant mass.
resultsThe proposed method is tested on a set with 323 contours including 143 benign masses and 180 malignant ones from digital database of screening mammography (DDSM). The best accuracy of classification is 99.66% using the feature of root mean square slope with SVM classifier.
conclusionThe performance of the proposed method is better than traditional method. In addition, RMS slope is an effective feature comparable to most of the existing features.
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