# MobileCaps: A Lightweight Model for Screening and Severity Analysis of COVID-19 Chest X-Ray Images

arXiv: https://arxiv.org/abs/2108.08775 · HTML: https://mlsanity.com/paper/2108.08775/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 50 references that are in the map.
- First posted on arXiv 19 Aug 2021.

## Builds on (most cited first, 15 of 15)

- [SGDR: Stochastic Gradient Descent with Warm Restarts](https://mlsanity.com/paper/1608.03983/) (2016)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [Dynamic Routing Between Capsules](https://mlsanity.com/paper/1710.09829/) (2017)
- [Snapshot Ensembles: Train 1, get M for free](https://mlsanity.com/paper/1704.00109/) (2017)
- [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)
- [COVID-19 Image Data Collection: Prospective Predictions Are the Future](https://mlsanity.com/paper/2006.11988/) (2020)
- [Deep Learning COVID-19 Features on CXR using Limited Training Data Sets](https://mlsanity.com/paper/2004.05758/) (2020)
- [COVID-CAPS: A Capsule Network-based Framework for Identification of COVID-19 cases from X-ray Images](https://mlsanity.com/paper/2004.02696/) (2020)
- [COVIDGR dataset and COVID-SDNet methodology for predicting COVID-19 based on Chest X-Ray images](https://mlsanity.com/paper/2006.01409/) (2020)
- [COVID-ResNet: A Deep Learning Framework for Screening of COVID19 from Radiographs](https://mlsanity.com/paper/2003.14395/) (2020)
- [Predicting COVID-19 Pneumonia Severity on Chest X-ray with Deep Learning](https://mlsanity.com/paper/2005.11856/) (2020)
- [DeepCOVIDExplainer: Explainable COVID-19 Diagnosis Based on Chest X-ray Images](https://mlsanity.com/paper/2004.04582/) (2020)
- [BS-Net: learning COVID-19 pneumonia severity on a large Chest X-Ray dataset](https://mlsanity.com/paper/2006.04603/) (2020)
- [Evaluating Generalization Ability of Convolutional Neural Networks and Capsule Networks for Image Classification via Top-2 Classification](https://mlsanity.com/paper/1901.10112/) (2019)
- [COVID-MobileXpert: On-Device COVID-19 Patient Triage and Follow-up using Chest X-rays](https://mlsanity.com/paper/2004.03042/) (2020)

## Built on by (most cited first, 1 of 1)

- [Cognizance of Post-COVID-19 Multi-Organ Dysfunction through Machine Learning Analysis](https://mlsanity.com/paper/2309.16736/) (2023)

## Abstract (arXiv)

The world is going through a challenging phase due to the disastrous effect caused by the COVID-19 pandemic on the healthcare system and the economy. The rate of spreading, post-COVID-19 symptoms, and the occurrence of new strands of COVID-19 have put the healthcare systems in disruption across the globe. Due to this, the task of accurately screening COVID-19 cases has become of utmost priority. Since the virus infects the respiratory system, Chest X-Ray is an imaging modality that is adopted extensively for the initial screening. We have performed a comprehensive study that uses CXR images to identify COVID-19 cases and realized the necessity of having a more generalizable model. We utilize MobileNetV2 architecture as the feature extractor and integrate it into Capsule Networks to construct a fully automated and lightweight model termed as MobileCaps. MobileCaps is trained and evaluated on the publicly available dataset with the model ensembling and Bayesian optimization strategies to efficiently classify CXR images of patients with COVID-19 from non-COVID-19 pneumonia and healthy cases. The proposed model is further evaluated on two additional RT-PCR confirmed datasets to demonstrate the generalizability. We also introduce MobileCaps-S and leverage it for performing severity assessment of CXR images of COVID-19 based on the Radiographic Assessment of Lung Edema (RALE) scoring technique. Our classification model achieved an overall recall of 91.60, 94.60, 92.20, and a precision of 98.50, 88.21, 92.62 for COVID-19, non-COVID-19 pneumonia, and healthy cases, respectively. Further, the severity assessment model attained an R2 coefficient of 70.51. Owing to the fact that the proposed models have fewer trainable parameters than the state-of-the-art models reported in the literature, we believe our models will go a long way in aiding healthcare systems in the battle against the pandemic.