In today’s world, brain stroke is considered as a life-threatening disease provoked by undesirable blockage among the arteries feeding the human brain. The timely diagnosis of this brain stroke detection in Magnetic Resonance Imaging (MRI) images increases the patient’s survival rate. However, automated detection plays a significant challenge owing to the complexity of the shape, dimension of size and the location of stroke lesions. In this paper, a novel optimized fuzzy level segmentation algorithm is proposed to detect the ischemic stroke lesions. After segmentation, the multi-textural features are extracted to form a feature set. These features are given as input to the proposed weighted Gaussian Naïve Bayes classifier to discriminate normal and abnormal stroke lesion classes. The experimental result manifests that the proposed methodology achieves a higher accuracy as compared with the existing state-of-the-art techniques. The proposed classifier discriminates normal and abnormal classes efficiently and attains 99.32% of accuracy, 96.87% of sensitivity and 98.82% of F1 measure.

中文翻译：

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使用加权高斯朴素贝叶斯分类器进行自动脑卒中分类的多特征分析

在当今世界，脑中风被认为是一种危及生命的疾病，由供养人脑的动脉之间的不良阻塞引起。在磁共振成像 (MRI) 图像中及时诊断这种脑卒中检测可提高患者的存活率。然而，由于中风病变的形状、尺寸尺寸和位置的复杂性，自动检测面临着巨大的挑战。在本文中，提出了一种新的优化模糊水平分割算法来检测缺血性中风病变。分割后，提取多纹理特征形成特征集。这些特征作为所提出的加权高斯朴素贝叶斯分类器的输入，以区分正常和异常中风病变类别。实验结果表明，与现有的最先进技术相比，所提出的方法实现了更高的准确性。所提出的分类器有效地区分正常和异常类别，准确率达到 99.32%，灵敏度达到 96.87%，F1 测量达到 98.82%。