ArticleComputational intelligence and neuroscience2021
Multi-Features-Based Automated Breast Tumor Diagnosis Using Ultrasound Image and Support Vector Machine.
Article in Computational intelligence and neuroscience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Wearable Ultrasound Sensing with Dual Arrays and Machine Learning for Real-Time Tremor Characterization and Antagonist Muscle Monitoring.IEEE transactions on instrumentation and measurement · 2026Article
- Deep learning-based prediction of nodal metastasis in lung cancer using endobronchial ultrasound.JTCVS techniques · 2024Article
- Ultrasound-based deep learning in the establishment of a breast lesion risk stratification system: a multicenter study.European radiology · 2023Article
- Research on Lung Ultrasound Image Classification Based on Compressed Sensing.Journal of healthcare engineering · 2022Article
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6 authors.
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Abstract
Breast ultrasound examination is a routine, fast, and safe method for clinical diagnosis of breast tumors. In this paper, a classification method based on multi-features and support vector machines was proposed for breast tumor diagnosis. Multi-features are composed of characteristic features and deep learning features of breast tumor images. Initially, an improved level set algorithm was used to segment the lesion in breast ultrasound images, which provided an accurate calculation of characteristic features, such as orientation, edge indistinctness, characteristics of posterior shadowing region, and shape complexity. Simultaneously, we used transfer learning to construct a pretrained model as a feature extractor to extract the deep learning features of breast ultrasound images. Finally, the multi-features were fused and fed to support vector machine for the further classification of breast ultrasound images. The proposed model, when tested on unknown samples, provided a classification accuracy of 92.5% for cancerous and noncancerous tumors.
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