Zhang Lifeng, Cui Hongyan, Hu Anming, Li Jiadong, Tang Yidi, Welsch Roy Elmer
School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing 100876, China.
State Key Laboratory of Networking & Switching Technology, Beijing University of the Posts and Telecommunications, Beijing 100876, China.
Diagnostics (Basel). 2022 Oct 26;12(11):2591. doi: 10.3390/diagnostics12112591.
Cerebral stroke (CS) is a heterogeneous syndrome caused by multiple disease mechanisms. Ischemic stroke (IS) is a subtype of CS that causes a disruption of cerebral blood flow with subsequent tissue damage. Noncontrast computer tomography (NCCT) is one of the most important IS detection methods. It is difficult to select the features of IS CT within computational image analysis. In this paper, we propose AC-YOLOv5, which is an improved detection algorithm for IS. The algorithm amplifies the features of IS via an NCCT image based on adaptive local region contrast enhancement, which then detects the region of interest via YOLOv5, which is one of the best detection algorithms at present. The proposed algorithm was tested on two datasets, and seven control group experiments were added, including popular detection algorithms at present and other detection algorithms based on image enhancement. The experimental results show that the proposed algorithm has a high accuracy (94.1% and 91.7%) and recall (85.3% and 88.6%) rate; the recall result is especially notable. This proves the excellent performance of the accuracy, robustness, and generalizability of the algorithm.
脑卒（CS）是一种由多种疾病机制引起的异质性综合征。缺血性脑卒中（IS）是CS的一种亚型，它会导致脑血流中断并随后造成组织损伤。非增强计算机断层扫描（NCCT）是最重要的IS检测方法之一。在计算图像分析中，很难选择出IS CT的特征。在本文中，我们提出了AC-YOLOv5，这是一种针对IS的改进检测算法。该算法基于自适应局部区域对比度增强，通过NCCT图像放大IS的特征，然后通过目前最佳检测算法之一的YOLOv5检测感兴趣区域。所提出的算法在两个数据集上进行了测试，并增加了七个对照组实验，包括目前流行的检测算法和其他基于图像增强的检测算法。实验结果表明，所提出的算法具有较高的准确率（94.1%和91.7%）和召回率（85.3%和88.6%）；召回结果尤其显著。这证明了该算法在准确性、鲁棒性和通用性方面具有出色的性能。
