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首页> 外文会议>International Joint Conference on Neural Networks >Stroke Lesion Detection Using Convolutional Neural Networks
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Stroke Lesion Detection Using Convolutional Neural Networks

机译：利用卷积神经网络检测脑卒中

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Stroke is an injury that affects the brain tissue, mainly caused by changes in the blood supply to a particular region of the brain. As consequence, some specific functions related to that affected region can be reduced, decreasing the quality of life of the patient. In this work, we deal with the problem of stroke detection in Computed Tomography (CT) images using Convolutional Neural Networks (CNN) optimized by Particle Swarm optimization (PSO). We considered two different kinds of strokes, ischemic and hemorrhagic, as well as making available a public dataset to foster the research related to stroke detection in the human brain. The dataset comprises three different types of images for each case, i.e., the original CT image, one with the segmented cranium and an additional one with the radiological density's map. The results evidenced that CNN's are suitable to deal with stroke detection, obtaining promising results.
机译：中风是一种影响大脑组织的损伤，主要是由大脑特定区域的血液供应变化引起的。结果，与该受影响区域有关的某些特定功能可能会减少，从而降低患者的生活质量。在这项工作中，我们使用由粒子群优化（PSO）优化的卷积神经网络（CNN）处理计算机断层扫描（CT）图像中的笔画检测问题。我们考虑了两种不同的中风，即缺血性和出血性，并提供了公共数据集来促进与人脑中风检测相关的研究。数据集包括每种情况的三种不同类型的图像，即原始CT图像，其中一种具有分割的颅骨，另一种具有放射线密度图。结果表明，CNN适用于中风检测，获得了可喜的结果。

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