利用特征提取提高冠状病毒疾病分类效率

Anis Fitri Nur Masruriyah, H. Basri, H. H. Handayani, Ahmad Fauzi, Ayu Ratna Juwita, Deden Wahiddin
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引用次数: 0

摘要

自2019年底以来,COVID-19一直是一场流行病。COVID-19患者人数继续增加,直到出现新的变体。COVID-19检测程序首先发现早期症状,然后通过拭子和胸部x射线方法确认。由于在胸部x光检查中有些患者的症状与肺炎相同,因此拭子和胸部x光检查的过程需要较长时间。本研究以离散小波变换为特征提取技术,以深度学习为分类方法,对COVID-19和非COVID-19进行分类。本研究结果能够识别COVID-19胸片,并且在支持向量机、决策树和深度学习上的准确率提高了10%以上。因此,对比结果表明,特征提取能够显著提高准确率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The Rise Efficiency of Coronavirus Disease Classification Employing Feature Extraction
COVID-19 has been an epidemic since the end of 2019. The number of patients with COVID-19 continues to escalate until new variants emerge. The COVID-19 detection procedure begins with detecting early symptoms, furthermore, confirmed by the swab and Chest X-Ray methods. The process of swab and Chest X-Ray takes a relatively long time since in Chest X-Ray some patients have the same symptoms as pneumonia. This study carried out the classification of COVID-19 and not COVID-19 with Discrete Wavelet Transform as feature extraction techniques and deep learning as the classification method. The result of this study capable to identify Chest X-Ray with COVID-19 and the accuracy increased of more than 10% on Support Vector Machine, Decision Tree and Deep Learning. So that, the comparison result showed that feature extraction was able to significantly improve accuracy.
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