Challenges, opportunities, and advances related to COVID-19 classification based on deep learning

被引：0
作者：
Agnihotri A. [1 ]
Kohli N. [1 ]
机构：
[1] Department of Computer Science and Engineering, Harcourt Butler Technical University, Uttar Pradesh, Kanpur
来源：
Data Science and Management | 2023年 / 6卷 / 02期
关键词：
CAD system; Classification; Coronavirus; COVID-19; Deep learning;
D O I：
10.1016/j.dsm.2023.03.005
中图分类号：
学科分类号：
摘要：
The novel coronavirus disease, or COVID-19, is a hazardous disease. It is endangering the lives of many people living in more than two hundred countries. It directly affects the lungs. In general, two main imaging modalities, i.e., computed tomography (CT) and chest x-ray (CXR) are used to achieve a speedy and reliable medical diagnosis. Identifying the coronavirus in medical images is exceedingly difficult for diagnosis, assessment, and treatment. It is demanding, time-consuming, and subject to human mistakes. In biological disciplines, excellent performance can be achieved by employing artificial intelligence (AI) models. As a subfield of AI, deep learning (DL) networks have drawn considerable attention than standard machine learning (ML) methods. DL models automatically carry out all the steps of feature extraction, feature selection, and classification. This study has performed comprehensive analysis of coronavirus classification using CXR and CT imaging modalities using DL architectures. Additionally, we have discussed how transfer learning is helpful in this regard. Finally, the problem of designing and implementing a system using computer-aided diagnostic (CAD) to find COVID-19 using DL approaches highlighted a future research possibility. © 2023 Xi'an Jiaotong University
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页码：98 / 109
页数：11
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