2019年冠状病毒病(COVID-19)
深度学习
人工智能
计算机科学
鉴定(生物学)
分割
图像(数学)
严重急性呼吸综合征冠状病毒2型(SARS-CoV-2)
图像分割
计算机视觉
2019-20冠状病毒爆发
模式识别(心理学)
医学
病毒学
病理
生物
植物
疾病
爆发
传染病(医学专业)
作者
Bhupendra Patel,Deep Kothadiya,Ronak Patel
标识
DOI:10.1109/aisc56616.2023.10085498
摘要
The whole world has been facing the problem of novel Coronavirus (COVID-19) since 2020. Over 88 million cases are confirmed and around 5 lacks deaths are accounted. Using the Lung-Computed Tomography (CT) Lesion Segmentation dataset, deep learning techniques may be used to quickly identify COVID-19 and the exact region that is infected. Based on CT, it is easy to identify the problem and the infected area, then assisting treatment of COVID-19. In the literature survey, research study has considered many research papers worked done work on identification of COVID-19 using chest/lungs X-ray image, and with that identified what are the deep learning-based models or methodology they have used for detecting COVID-19 result. To overcome their result, Authors have proposed a latest methodology of deep learning with the YOLO variant 7x to get optimum result of COVID -19 detection from lungs X-ray image. To identify COVID-19, Authors have applied proposed methodology on publically avail X-ray image-based dataset of COVID-19, proposed methodology has achieved good performance to detect COVID infection from lungs.
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