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Around 450 million people are affected by pneumonia every year which resultsin 2.5 million deaths. Covid-19 has also affected 181 million people which haslead to 3.92 million casualties. The chances of death in both of these diseasescan be significantly reduced if they are diagnosed early. However, the currentmethods of diagnosing pneumonia (complaints + chest X-ray) and covid-19(RT-PCR) require the presence of expert radiologists and time, respectively.With the help of Deep Learning models, pneumonia and covid-19 can be detectedinstantly from Chest X-rays or CT scans. This way, the process of diagnosingPneumonia/Covid-19 can be made more efficient and widespread. In this paper, weaim to elicit, explain, and evaluate, qualitatively and quantitatively, majoradvancements in deep learning methods aimed at detecting or localizingcommunity-acquired pneumonia (CAP), viral pneumonia, and covid-19 from imagesof chest X-rays and CT scans. Being a systematic review, the focus of thispaper lies in explaining deep learning model architectures which have eitherbeen modified or created from scratch for the task at hand wiwth focus ongeneralizability. For each model, this paper answers the question of why themodel is designed the way it is, the challenges that a particular modelovercomes, and the tradeoffs that come with modifying a model to the requiredspecifications. A quantitative analysis of all models described in the paper isalso provided to quantify the effectiveness of different models with a similargoal. Some tradeoffs cannot be quantified, and hence they are mentionedexplicitly in the qualitative analysis, which is done throughout the paper. Bycompiling and analyzing a large quantum of research details in one place withall the datasets, model architectures, and results, we aim to provide aone-stop solution to beginners and current researchers interested in thisfield.

Advancement of Deep Learning in Pneumonia and Covid-19 Classification and Localization: A Qualitative and Quantitative Analysis