图像处理技术及其在决策支持系统中应用卷积神经网络进行Covid-19疾病预测的性能分析

K. Ravishankar, C. Jothikumar
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引用次数: 1

摘要

近年来,新冠肺炎疫情已成为人类社会面临的重大问题。根据不同的症状,如咳嗽、发烧、头痛、上气不接下气等,可以确定任何人是否受到感染。然而,大多数症状是由各种其他疾病共有的,这使得医生很难识别感染。为了帮助医生,设计了许多方法,使用不同的特征,如血液报告、肺和心脏特征来检测疾病。该方法利用磁共振成像扫描装置捕获肺部图像并记录心脏特征。利用图像提取肺特征,并从心脏图中提取心脏特征。同样,从血液样本中提取特征。通过从人身上提取这些特征,该方法估计不同的体重来预测疾病。不同的方法以不同的方式估计样本的相似度来对输入样本进行分类。然而,在医学领域,图像处理技术被用于解决不同的问题;同样的方法也被用于疾病的检测。此外,通过各种方法使用不同的特征集来检测Covid-19的存在。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Analysis Performance Of Image Processing Technique Its Application by Decision Support Systems On Covid-19 Disease Prediction Using Convolution Neural Network
The Covid-19 pandemic has been identified as a key issue for human society, in recent times. The presence of the infection on any human is identified according to different symptoms like cough, fever, headache, breathless and so on. However, most of the symptoms are shared by various other diseases, which makes it challenging for the medical practitioners to identify the infection. To aid the medical practitioners, there are a number of approaches designed which use different features like blood report, lung and cardiac features to detect the disease. The method captures the lung image using magnetic resonance imaging scan device and records the cardiac features. Using the image, the lung features are extracted and from the cardiac graph, the cardiac features are extracted. Similarly, from the blood samples, the features are extracted. By extracting such features from the person, the method estimates different weight measures to predict the disease. Different methods estimate the similarity of the samples in different ways to classify the input sample. However, the image processing techniques are used for different problems in medical domain; the same has been used in the detection of the disease. Also, the presence of Covid-19 is detected using different set of features by various approaches.
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