ArticleComputational intelligence and neuroscience2022
Tomographic Ultrasound Imaging in the Diagnosis of Breast Tumors under the Guidance of Deep Learning Algorithms.
Article in Computational intelligence and neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed, 4 citations in OpenAlex.
- Review
- A Machine Learning Framework for Diagnosing and Predicting the Severity of Coronary Artery Disease.Reviews in cardiovascular medicine · 2023Article
- Correlation Analysis of Ultrasound Elastography Score with Invasive Breast Cancer and Biological Prognostic Factors.Contrast media & molecular imaging · 2022Article
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study was aimed to discuss the feasibility of distinguishing benign and malignant breast tumors under the tomographic ultrasound imaging (TUI) of deep learning algorithm. The deep learning algorithm was used to segment the images, and 120 patients with breast tumor were included in this study, all of whom underwent routine ultrasound examinations. Subsequently, TUI was used to assist in guiding the positioning, and the light scattering tomography system was used to further measure the lesions. A deep learning model was established to process the imaging results, and the pathological test results were undertaken as the gold standard for the efficiency of different imaging methods to diagnose the breast tumors. The results showed that, among 120 patients with breast tumor, 56 were benign lesions and 64 were malignant lesions. The average total amount of hemoglobin (HBT) of malignant lesions was significantly higher than that of benign lesions (
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