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An investigation of the classification accuracy of a deep learning framework-based computer-aided diagnosis system in different pathological types of breast lesions

   
@article{JTD34051,
	author = {Mengsu Xiao and Chenyang Zhao and Qingli Zhu and Jing Zhang and He Liu and Jianchu Li and Yuxin Jiang},
	title = {An investigation of the classification accuracy of a deep learning framework-based computer-aided diagnosis system in different pathological types of breast lesions},
	journal = {Journal of Thoracic Disease},
	volume = {11},
	number = {12},
	year = {2019},
	keywords = {},
	abstract = {Background: Deep learning-based computer-aided diagnosis (CAD) is an important method in aiding diagnosis for radiologists. We investigated the accuracy of a deep learning-based CAD in classifying breast lesions with different histological types.
Methods: A total of 448 breast lesions were detected on ultrasound (US) and classified by an experienced radiologist, a resident and deep learning-based CAD respectively. The pathological results of the lesions were chosen as the golden standard. The diagnostic performances of the three raters in different pathological types were analyzed.
Results: For the overall diagnostic performance, deep learning-based CAD presented a significantly higher specificity (76.96%) compared with the two radiologists. The area under ROC of CAD was almost equal with the experienced radiologist (0.81 vs. 0.81), while significantly higher than the resident (0.81 vs. 0.70, P},
	issn = {2077-6624},	url = {https://jtd.amegroups.org/article/view/34051}
}