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

arXiv: https://arxiv.org/abs/2111.08606 · HTML: https://mlsanity.com/paper/2111.08606/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 26 of its 64 references that are in the map.
- First posted on arXiv 16 Nov 2021.

## Builds on (most cited first, 26 of 26)

- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs](https://mlsanity.com/paper/1606.00915/) (2016)
- [3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation](https://mlsanity.com/paper/1606.06650/) (2016)
- [ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases](https://mlsanity.com/paper/1705.02315/) (2017)
- [CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning](https://mlsanity.com/paper/1711.05225/) (2017)
- [R-FCN: Object Detection via Region-based Fully Convolutional Networks](https://mlsanity.com/paper/1605.06409/) (2016)
- [Residual Attention Network for Image Classification](https://mlsanity.com/paper/1704.06904/) (2017)
- [Confounding variables can degrade generalization performance of radiological deep learning models](https://mlsanity.com/paper/1807.00431/) (2018)
- [COVID-Net: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases from Chest X-Ray Images](https://mlsanity.com/paper/2003.09871/) (2020)
- [Cascaded Partial Decoder for Fast and Accurate Salient Object Detection](https://mlsanity.com/paper/1904.08739/) (2019)
- [Can AI help in screening Viral and COVID-19 pneumonia?](https://mlsanity.com/paper/2003.13145/) (2020)
- [Inf-Net: Automatic COVID-19 Lung Infection Segmentation from CT Images](https://mlsanity.com/paper/2004.14133/) (2020)
- [Receptive Field Block Net for Accurate and Fast Object Detection](https://mlsanity.com/paper/1711.07767/) (2017)
- [3D Anisotropic Hybrid Network: Transferring Convolutional Features from 2D Images to 3D Anisotropic Volumes](https://mlsanity.com/paper/1711.08580/) (2017)
- [Dual-Sampling Attention Network for Diagnosis of COVID-19 from Community Acquired Pneumonia](https://mlsanity.com/paper/2005.02690/) (2020)
- [Deep Learning for Automatic Pneumonia Detection](https://mlsanity.com/paper/2005.13899/) (2020)
- [CoupleNet: Coupling Global Structure with Local Parts for Object Detection](https://mlsanity.com/paper/1708.02863/) (2017)
- [MUXConv: Information Multiplexing in Convolutional Neural Networks](https://mlsanity.com/paper/2003.13880/) (2020)
- [Detection of Coronavirus (COVID-19) Associated Pneumonia based on Generative Adversarial Networks and a Fine-Tuned Deep Transfer Learning Model using Chest X-ray Dataset](https://mlsanity.com/paper/2004.01184/) (2020)
- [Artificial Intelligence applied to chest X-Ray images for the automatic detection of COVID-19. A thoughtful evaluation approach](https://mlsanity.com/paper/2011.14259/) (2020)
- [An Adversarial Approach for the Robust Classification of Pneumonia from Chest Radiographs](https://mlsanity.com/paper/2001.04051/) (2020)
- [Pneumonia Detection in Chest Radiographs](https://mlsanity.com/paper/1811.08939/) (2018)

## Abstract (arXiv)

Around 450 million people are affected by pneumonia every year which results in 2.5 million deaths. Covid-19 has also affected 181 million people which has lead 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 (RT-PCR) 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 CT scans. This way, the process of diagnosing Pneumonia/Covid-19 can be made more efficient and widespread. In this paper, we aim to elicit, explain, and evaluate, qualitatively and quantitatively, major advancements in deep learning methods aimed at detecting or localizing community-acquired pneumonia (CAP), 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 deep learning model architectures which have either been modified or created from scratch for the task at hand wiwth focus on generalizability. 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 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 datasets, model architectures, and results, we aim to provide a one-stop solution to beginners and current researchers interested in this field.