Deep CNN based Multi Classification of Respiratory Disease using X-Ray Images

S. Varalakshmi, V. P, R. V
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Abstract

COVID-19 debuted in Wuhan, China on December 19, 2019. In a brief period, deadly virus now migrated to practically every country. To avoid the causative agent COVID-19 disease, governments implement a number of strict restrictions, notably prohibiting people from leaving their homes. This paper focused on detecting and classifying disease such as viral pneu-monia, covidand normal from x-ray images using deep learning methods along with pre-trained models. Moreover, validation accuracy of CNN model attained around 91 % while performing layers in neural network. Several investigations examined that identifying disease of covid reached more accuracy around 98% with hybrid and other algorithms without removing noise from particular images. But this work mainly focused on normalizing images to make the computation very efficient, convergence faster too.
基于深度CNN的呼吸系统疾病x射线图像多重分类
2019年12月19日,新冠肺炎在中国武汉首次出现。在很短的时间内,致命的病毒现在几乎传播到了每一个国家。为了避免病原体COVID-19疾病,各国政府实施了一系列严格的限制措施,特别是禁止人们离开家园。本文的重点是使用深度学习方法和预训练模型从x射线图像中检测和分类病毒性肺炎、新冠病毒和正常疾病。此外,在神经网络中进行分层时,CNN模型的验证准确率达到91%左右。几项调查表明,在不去除特定图像噪声的情况下,使用混合算法和其他算法识别新冠肺炎的准确率达到了98%左右。但是这项工作主要集中在对图像进行归一化,使计算非常高效,收敛速度也更快。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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