Emami Mohammad, Tinati Mohammad Ali, Musevi Niya Javad, Danishvar Sebelan
Department, Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz 51666-16471, Iran.
College of Engineering, Design and Physical Sciences, Brunel University London, Uxbridge UB8 3PH, UK.
Biomimetics (Basel). 2025 Aug 4;10(8):509. doi: 10.3390/biomimetics10080509.
A stroke is a critical medical condition and one of the leading causes of death among humans. Segmentation of the lesions of the brain in which the blood flow is impeded because of blood coagulation plays a vital role in drug prescription and medical diagnosis. Computed tomography (CT) scans play a crucial role in detecting abnormal tissue. There are several methods for segmenting medical images that utilize the main images without considering the patient's privacy information. In this paper, a deep network is proposed that utilizes compressive sensing and ensemble learning to protect patient privacy and segment the dataset efficiently. The compressed version of the input CT images from the ISLES challenge 2018 dataset is applied to the ensemble part of the proposed network, which consists of two multi-resolution modified U-shaped networks. The evaluation metrics of accuracy, specificity, and dice coefficient are 92.43%, 91.3%, and 91.83%, respectively. The comparison to the state-of-the-art methods confirms the efficiency of the proposed compressive sensing-based ensemble net (CS-Ensemble Net). The compressive sensing part provides information privacy, and the parallel ensemble learning produces better results.
中风是一种严重的医学病症，也是人类主要死因之一。因血液凝固导致血流受阻的脑部病变分割在药物处方和医学诊断中起着至关重要的作用。计算机断层扫描（CT）扫描在检测异常组织方面发挥着关键作用。有几种分割医学图像的方法，这些方法利用主图像而不考虑患者的隐私信息。本文提出了一种深度网络，该网络利用压缩感知和集成学习来保护患者隐私并有效分割数据集。来自2018年ISLES挑战赛数据集的输入CT图像的压缩版本被应用于所提出网络的集成部分，该集成部分由两个多分辨率改进的U形网络组成。准确率、特异性和骰子系数的评估指标分别为92.43%、91.3%和91.83%。与现有方法的比较证实了所提出的基于压缩感知的集成网络（CS-Ensemble Net）的有效性。压缩感知部分提供信息隐私，并行集成学习产生更好的结果。
