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In this paper, a classification method based on multi\u2010features and support vector machines was proposed for breast tumor diagnosis. Multi\u2010features 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\u2010features 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.<\/jats:p>","DOI":"10.1155\/2021\/9980326","type":"journal-article","created":{"date-parts":[[2021,5,19]],"date-time":"2021-05-19T23:50:07Z","timestamp":1621468207000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Multi\u2010Features\u2010Based Automated Breast Tumor Diagnosis Using Ultrasound Image and Support Vector Machine"],"prefix":"10.1155","volume":"2021","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7236-6859","authenticated-orcid":false,"given":"Zhemin","family":"Zhuang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0709-0411","authenticated-orcid":false,"given":"Zengbiao","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8782-7996","authenticated-orcid":false,"given":"Shuxin","family":"Zhuang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1505-3159","authenticated-orcid":false,"given":"Alex Noel","family":"Joseph Raj","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1089-9518","authenticated-orcid":false,"given":"Ye","family":"Yuan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1695-3618","authenticated-orcid":false,"given":"Ruban","family":"Nersisson","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2021,5,19]]},"reference":[{"key":"e_1_2_11_1_2","doi-asserted-by":"publisher","DOI":"10.3322\/caac.21492"},{"key":"e_1_2_11_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.measurement.2019.05.083"},{"key":"e_1_2_11_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2018.12.006"},{"key":"e_1_2_11_4_2","doi-asserted-by":"publisher","DOI":"10.1148\/rg.2019190087"},{"key":"e_1_2_11_5_2","doi-asserted-by":"crossref","unstructured":"ChangJ. 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