Brain stroke is an emergency medical condition which occurs mainly due to insufficient blood flow to the brain. It results in permanent cellular-level damage. There are two main types of brain stroke, ischemic and hemorrhagic. Ischemic brain stroke is caused by a lack of blood flow, and the haemorrhagic form is due to internal bleeding. The affected part of brain will not function properly after this attack. Hence, early detection is important for more efficacious treatment. Computer-aided diagnosis is a type of non-invasive diagnostic tool which can help in detecting life-threatening disease in its early stage by utilizing image processing and soft computing techniques. In this paper, we have developed one such model to assess intracerebral haemorrhage by employing non-linear features combined with a probabilistic neural network classifier and computed tomography (CT) images. Our model achieved a maximum accuracy of 97.37% in discerning normal versus haemorrhagic subjects. An intracerebral haemorrhage index is also developed using only three significant features. The clinical and statistical validation of the model confirms its suitability in providing for improved treatment planning and in making strategic decisions.

脑卒中是一种紧急医疗状况，主要由于血液流向大脑不足而发生。这会导致永久的细胞水平损伤。脑卒中有两种主要类型，即缺血性和出血性。缺血性脑卒中是由于血液缺乏引起的，出血形式是由于内部出血引起的。发作后大脑受影响的部分将无法正常运行。因此，早期发现对于更有效的治疗很重要。计算机辅助诊断是一种非侵入性诊断工具，可以通过利用图像处理和软计算技术在早期阶段帮助检测威胁生命的疾病。在本文中，我们已经开发出一种这样的模型，通过使用非线性特征结合概率神经网络分类器和计算机断层扫描（CT）图像来评估脑出血。我们的模型在识别正常人与出血性受试者之间达到了97.37％的最大准确性。脑出血指数也仅使用三个重要特征得到发展。该模型的临床和统计验证证实了其在提供改进的治疗计划和制定战略决策方面的适用性。