Combining support vector machine with genetic algorithm to classify ultrasound breast tumor images
Abstract
To promote the classification accuracy and decrease the time of extracting features and finding (near) optimal classification model of an ultrasound breast tumor image computer-aided diagnosis system, we propose an approach which simultaneously combines feature selection and parameter setting in this study. In our approach ultrasound breast tumors were segmented automatically by a level set method. The auto-covariance texture features and morphologic features were first extracted following the use of a genetic algorithm to detect significant features and determine the near-optimal parameters for the support vector machine (SVM) to identify the tumor as benign or malignant. The proposed CAD system can differentiate benign from malignant breast tumors with high accuracy and short feature extraction time. According to the experimental results, the accuracy of the proposed CAD system for classifying breast tumors is 95.24% and the computing time of the proposed system for calculating features of all breast tumor images is only 8% of that of a system without feature selection. Furthermore, the time of finding (near) optimal classification model is significantly than that of grid search. It is therefore clinically useful in reducing the number of biopsies of benign lesions and offers a second reading to assist inexperienced physicians in avoiding misdiagnosis.
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Single feature often cannot detect all fine details of a US image, and fusion of features outperforms single feature by providing more latent and intrinsic information.
“Single feature often cannot detect all the fine details of the US image, and fusion of features outperforms single feature by providing more latent and intrinsic information12346.”
From Discriminative Learning for Automatic Staging of Placental Maturity via Multi-layer Fisher Vector (2015) · cites this paper as [1]
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Discriminative Learning for Automatic Staging of Placental Maturity via Multi-layer Fisher Vector
Lei, B., Yao, Y., Chen, S., Li, S., Li, W., Ni, D., & Wang, T. (2015) · Scientific Reports · doi:10.1038/srep12818 · Cited by 28 · see citing works
- Authors
- Wu, W.-J., Lin, S.-W., & Moon, W. K.
- Affiliations
- Chang Gung University (TW)
- Seoul National University Hospital (KR)
- Corresponding author
- Lin, Shih-Wei
- Venue
- Computerized Medical Imaging and Graphics
- Publisher
- Elsevier BV
- Published
- 2012-12
- Volume
- 36
- Issue
- 8
- Pages
- 627-633
- ISSN
- 0730-4862 (ISSN-L)
- 0895-6111
- OpenAlex
- W2167536222
- Cited by
- 99
- References
- 48
- Citations by year
- 2026: 4 · 2025: 7 · 2024: 5 · 2023: 2 · 2022: 5 · 2021: 9 · 2020: 6 · 2019: 15 · 2018: 9 · 2017: 9 · 2016: 7 · 2015: 10 · 2014: 6 · 2013: 5
- FWCI
- 6.61
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Computerized Medical Imaging and Graphics (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Science Council
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Retrieval and Classification Techniques
- Keywords
- breast tumor classification, computer-aided diagnosis, support vector machine, genetic algorithm, feature selection, parameter optimization, level set segmentation, morphologic features, benign versus malignant tumors, biopsy reduction
- MeSH terms
- Adolescent, Adult, Algorithms, Breast Neoplasms, Female, Humans, Image Enhancement, Image Interpretation, Computer-Assisted, Middle Aged, Pattern Recognition, Automated, Sensitivity and Specificity, Reproducibility of Results, Ultrasonography, Mammary, Young Adult, Support Vector Machine
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