基于CNN算法的脑卒中检测

Prasad Gahiwad, Nilesh Deshmane, Sachet Karnakar, Sujit Mali, Rohini. G. Pise
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引用次数: 0

摘要

中风会损害中枢神经系统,是当今导致死亡的主要原因之一。与几种中风相比,出血性和缺血性中风对人体中枢神经系统有负面影响。作为脑血管健康状况之一,中风对一个人的生命和健康有着重大的影响。为了诊断和治疗中风,必须对脑CT扫描图像进行电子定量分析。深度神经网络具有强大的数据学习能力,是损伤揭示的重要工具。在本文中,我们的目标是利用卷积神经网络在ct扫描图像的帮助下检测脑卒中。在包含2551张图像的ct扫描数据集上对模型进行训练和测试后,我们获得了90%的最佳准确率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Brain Stroke Detection Using CNN Algorithm
Strokes damage the central nervous system and are one of the leading causes of death today. Compared with several kinds of stroke, hemorrhagic and ischemic causes have a negative impact on the human central nervous system. One of the cerebrovascular health conditions, stroke has a significant impact on a person’s life and health. In order to diagnose and treat stroke, brain CT scan images must undergo electronic quantitative analysis. An essential tool for damage revelation is provided by deep neural networks, which have a tremendous capacity for data learning. In this paper, we aim to detect brain strokes with the help of CT-Scan images by using a convolutional neural network. After training and testing the model on a CT-scan dataset comprising 2551 images, we obtained the best accuracy of 90%.
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