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Advancement of deep learning in pneumonia/Covid-19 classification and localization: A systematic review with qualitative and quantitative analysis.
Shah, Aakash; Shah, Manan.
  • Shah A; Department of Computer Science & Engineering, Institute of Technology Nirma University Ahmedabad India.
  • Shah M; Department of Chemical Engineering, School of Technology Pandit Deendayal Energy University Gandhinagar India.
Chronic Dis Transl Med ; 8(3): 154-171, 2022 Sep.
Article in English | MEDLINE | ID: covidwho-1772677
ABSTRACT
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.
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Full text: Available Collection: International databases Database: MEDLINE Type of study: Experimental Studies / Qualitative research / Reviews / Systematic review/Meta Analysis Language: English Journal: Chronic Dis Transl Med Year: 2022 Document Type: Article

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Full text: Available Collection: International databases Database: MEDLINE Type of study: Experimental Studies / Qualitative research / Reviews / Systematic review/Meta Analysis Language: English Journal: Chronic Dis Transl Med Year: 2022 Document Type: Article