ArticleNan fang yi ke da xue xue bao = Journal of Southern Medical University2019
[Establishment of a deep feature-based classification model for distinguishing benign and malignant breast tumors on full-filed digital mammography].
Article in Nan fang yi ke da xue xue bao = Journal of Southern Medical University, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed.
- Accurate segmentation algorithm of acoustic neuroma in the cerebellopontine angle based on ACP-TransUNet.Frontiers in neuroscience · 2023Article
- ELF5 inhibits the proliferation and invasion of breast cancer cells by regulating CD24.Molecular biology reports · 2021Article
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6 authors.
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Abstract
objectiveTo develop a deep features-based model to classify benign and malignant breast lesions on full- filed digital mammography.
methodsThe data of full-filed digital mammography in both craniocaudal view and mediolateral oblique view from 106 patients with breast neoplasms were analyzed. Twenty-three handcrafted features (HCF) were extracted from the images of the breast tumors and a suitable feature set of HCF was selected using
resultsQuantitative evaluation results showed that the proposed HCF+DF model outperformed HCF model, and AlexNet produced the best performances among the 3 deep learning models.
conclusionsThe proposed model that combines DF and HCF sets of breast tumors can effectively distinguish benign and malignant breast lesions on full-filed digital mammography.
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