https://scholars.lib.ntu.edu.tw/handle/123456789/634871
標題: | Examining the effectiveness of a deep learning-based computer-aided breast cancer detection system for breast ultrasound | 作者: | Fujioka, Tomoyuki Kubota, Kazunori Hsu, Jen Feng RUEY-FENG CHANG Sawada, Terumasa Ide, Yoshimi Taruno, Kanae Hankyo, Meishi Kurita, Tomoko Nakamura, Seigo Tateishi, Ukihide Takei, Hiroyuki |
關鍵字: | Artificial intelligence | Breast cancer | Computer-aided detection | Deep learning | Ultrasound | 公開日期: | 1-一月-2023 | 來源出版物: | Journal of Medical Ultrasonics | 摘要: | Purpose: This study aimed to evaluate the clinical usefulness of a deep learning-based computer-aided detection (CADe) system for breast ultrasound. Methods: The set of 88 training images was expanded to 14,000 positive images and 50,000 negative images. The CADe system was trained to detect lesions in real- time using deep learning with an improved model of YOLOv3-tiny. Eighteen readers evaluated 52 test image sets with and without CADe. Jackknife alternative free-response receiver operating characteristic analysis was used to estimate the effectiveness of this system in improving lesion detection. Result: The area under the curve (AUC) for image sets was 0.7726 with CADe and 0.6304 without CADe, with a 0.1422 difference, indicating that with CADe was significantly higher than that without CADe (p < 0.0001). The sensitivity per case was higher with CADe (95.4%) than without CADe (83.7%). The specificity of suspected breast cancer cases with CADe (86.6%) was higher than that without CADe (65.7%). The number of false positives per case (FPC) was lower with CADe (0.22) than without CADe (0.43). Conclusion: The use of a deep learning-based CADe system for breast ultrasound by readers significantly improved their reading ability. This system is expected to contribute to highly accurate breast cancer screening and diagnosis. |
URI: | https://scholars.lib.ntu.edu.tw/handle/123456789/634871 | ISSN: | 13464523 | DOI: | 10.1007/s10396-023-01332-9 |
顯示於: | 資訊工程學系 |
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