Advancement of deep learning in pneumonia/Covid-19 classification and localization:A systematic review with qualitative and quantitative analysis

作     者：Aakash Shah Manan Shah Aakash Shah;Manan Shah

作者机构：Department of Computer Science&EngineeringInstute of TechnologyNirma UniversityAhmedabadIndia Department of Chemical EngineeringSchool of TechnologyPandit Deendayal Energy UniversityGandhinagarIndia 

出 版 物：《Chronic Diseases and Translational Medicine》 (慢性疾病与转化医学（英文版）)

年 卷 期：2022年第8卷第3期

页      面：154-171页

核心收录：

学科分类：1002[医学-临床医学] 10[医学] 

主　　题：classification Covid-19 deep learning localization pneumonia 

摘      要：Around 450 million people are affected by pneumonia every year,which results in 2.5 million deaths.Coronavirus disease 2019(Covid-19)has also affected 181 million people,which led to 3.92 million casualties.The chances of death in both of these diseases can be significantly reduced if they are diagnosed early.However,the current methods of diagnosing pneumonia(complaints+chest X-ray)and Covid-19(real-time polymerase chain reaction)require the presence of expert radiologists and time,respectively.With the help of deep learning models,pneumonia and Covid-19 can be detected instantly from chest X-rays or computerized tomography(CT)scans.The process of diagnosing pneumonia/Covid-19 can become faster and more widespread.In this paper,we aimed to elicit,explain,and evaluate qualitatively and quantitatively all advancements in deep learning methods aimed at detecting community-acquired pneumonia,viral pneumonia,and Covid-19 from images of chest X-rays and CT scans.Being a systematic review,the focus of this paper lies in explaining various deep learning model architectures,which have either been modified or created from scratch for the task at hand.For each model,this paper answers the question of why the model is designed the way it is,the challenges that a particular model overcomes,and the tradeoffs that come with modifying a model to the required specifications.A grouped quantitative analysis of all models described in the paper is also provided to quantify the effectiveness of different models with a similar goal.Some tradeoffs cannot be quantified and,hence,they are mentioned explicitly in the qualitative analysis,which is done throughout the paper.By compiling and analyzing a large quantum of research details in one place with all the data sets,model architectures,and results,we aimed to provide a one-stop solution to beginners and current researchers interested in this field.