Deep Learning for Detection and Prediction of Covid-19 Virus on CT-Scan Image Dataset

A. Agrawal, Asadi Srinivasulu
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Abstract

As we all know, COVID-19 appears to be having a terrible impact on world health and well-being. Furthermore, at its peak, the COVID-19 cases worldwide reached a huge number i.e., in millions. The objective of the present work is to develop a model that detects COVID-19 utilizing CT-Scan Image Dataset and DL Techniques. As the number of verified cases rises, it becomes more critical to monitor and precisely categorize healthy and infected people. RT-PCR testing is the most used approach for the detection of Covid-19. However, several investigations have found that it has a low sensitivity in the early stages. Computer tomography (CT) is also used to detect image-morphological patterns of COVID-19-related chest lesions. The RT-PCR technique for diagnosing COVID has some drawbacks. For starters, test kits are insufficiently available, necessitating greater testing time and the sensitivity of testing varies. Therefore, employing CT scan pictures to screen COVID-19 is essential. The results showed that CT scan pictures might efficiently identify COVID-19, saving more lives. A Convolutional Neural Network (CNN) is a sort of Artificial Neural Network that is commonly used for image/object detection with class. An Input layer, Hidden layers, and an Output layer are common components of a neural network (NN). CNN is inspired by the brain's architecture. Artificial neurons or nodes in CNNs, like neurons in the brain, take inputs, process them, and deliver the result as output. Illness severity can be detected and calculated for future scopes and research. Another challenge encountered when dealing with severity infection detection and extending the existing work by using frameworks in order to increase accuracy. The proposed ECNN technique outperformed than CNN in terms of accuracy (95.35), execution time, and performance. This study could be extended or improved in the future by directing severity identification on the CT-Scan image dataset.
基于ct扫描图像数据集的Covid-19病毒深度学习检测与预测
众所周知,2019冠状病毒病似乎对世界健康和福祉产生了可怕的影响。此外,在高峰时期,全球新冠肺炎病例达到了数百万。本研究的目的是开发一个利用ct扫描图像数据集和DL技术检测COVID-19的模型。随着确诊病例数量的增加,对健康和受感染人群进行监测和精确分类变得更加重要。RT-PCR检测是检测Covid-19最常用的方法。然而,几项调查发现,它在早期阶段的灵敏度很低。计算机断层扫描(CT)也用于检测与covid -19相关的胸部病变的图像形态学模式。RT-PCR诊断新冠肺炎存在一些缺陷。对于初学者来说,测试工具的可用性不足,需要更长的测试时间和测试的灵敏度变化。因此,利用CT扫描图像来筛查COVID-19至关重要。结果表明,CT扫描图像可以有效识别COVID-19,挽救更多生命。卷积神经网络(CNN)是一种人工神经网络,通常用于类图像/对象检测。输入层、隐藏层和输出层是神经网络(NN)的常见组成部分。CNN的灵感来自于大脑的结构。cnn中的人工神经元或节点,就像大脑中的神经元一样,接受输入,处理它们,并将结果作为输出。可以检测和计算疾病严重程度,以供将来的范围和研究。在处理严重感染检测和通过使用框架扩展现有工作以提高准确性时遇到的另一个挑战。本文提出的ECNN技术在准确率(95.35)、执行时间和性能方面都优于CNN。这项研究可以在未来通过指导ct扫描图像数据集的严重性识别来扩展或改进。
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