# COVID-19 Infection Localization and Severity Grading from Chest X-ray Images

arXiv: https://arxiv.org/abs/2103.07985 · HTML: https://mlsanity.com/paper/2103.07985/

## Key facts

- Cited by 46 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 61 references that are in the map.
- First posted on arXiv 14 Mar 2021.

## Builds on (most cited first, 25 of 25)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [UNet++: A Nested U-Net Architecture for Medical Image Segmentation](https://mlsanity.com/paper/1807.10165/) (2018)
- [Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning](https://mlsanity.com/paper/1602.07261/) (2016)
- [PadChest: A large chest x-ray image dataset with multi-label annotated reports](https://mlsanity.com/paper/1901.07441/) (2019)
- [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)
- [Can AI help in screening Viral and COVID-19 pneumonia?](https://mlsanity.com/paper/2003.13145/) (2020)
- [Covid-19: Automatic detection from X-Ray images utilizing Transfer Learning with Convolutional Neural Networks](https://mlsanity.com/paper/2003.11617/) (2020)
- [Deep Learning COVID-19 Features on CXR using Limited Training Data Sets](https://mlsanity.com/paper/2004.05758/) (2020)
- [CovidGAN: Data Augmentation Using Auxiliary Classifier GAN for Improved Covid-19 Detection](https://mlsanity.com/paper/2103.05094/) (2021)
- [Review of Artificial Intelligence Techniques in Imaging Data Acquisition, Segmentation and Diagnosis for COVID-19](https://mlsanity.com/paper/2004.02731/) (2020)
- [Reliable Tuberculosis Detection using Chest X-ray with Deep Learning, Segmentation and Visualization](https://mlsanity.com/paper/2007.14895/) (2020)
- [Automatic Brain Tumor Detection and Segmentation Using U-Net Based Fully Convolutional Networks](https://mlsanity.com/paper/1705.03820/) (2017)
- [Self-Organized Operational Neural Networks with Generative Neurons](https://mlsanity.com/paper/2004.11778/) (2020)
- [Self-Organized Operational Neural Networks for Severe Image Restoration Problems](https://mlsanity.com/paper/2008.12894/) (2020)
- [COVID-19 Infection Map Generation and Detection from Chest X-Ray Images](https://mlsanity.com/paper/2009.12698/) (2020)
- [Iteratively Pruned Deep Learning Ensembles for COVID-19 Detection in Chest X-rays](https://mlsanity.com/paper/2004.08379/) (2020)
- [Extracting possibly representative COVID-19 Biomarkers from X-Ray images with Deep Learning approach and image data related to Pulmonary Diseases](https://mlsanity.com/paper/2004.00338/) (2020)
- [Convolutional Sparse Support Estimator Based Covid-19 Recognition from X-ray Images](https://mlsanity.com/paper/2005.04014/) (2020)
- [Large-Scale Screening of COVID-19 from Community Acquired Pneumonia using Infection Size-Aware Classification](https://mlsanity.com/paper/2003.09860/) (2020)
- [RANDGAN: Randomized Generative Adversarial Network for Detection of COVID-19 in Chest X-ray](https://mlsanity.com/paper/2010.06418/) (2020)
- [Deep Learning for Reliable Classification of COVID-19, MERS, and SARS from Chest X-Ray Images](https://mlsanity.com/paper/2005.11524/) (2020)
- [Exploring the Effect of Image Enhancement Techniques on COVID-19 Detection using Chest X-rays Images](https://mlsanity.com/paper/2012.02238/) (2020)

## Built on by (most cited first, 30 of 46)

- [SAM on Medical Images: A Comprehensive Study on Three Prompt Modes](https://mlsanity.com/paper/2305.00035/) (2023)
- [OSegNet: Operational Segmentation Network for COVID-19 Detection using Chest X-ray Images](https://mlsanity.com/paper/2202.10185/) (2022)
- [BiomedCoOp: Learning to Prompt for Biomedical Vision-Language Models](https://mlsanity.com/paper/2411.15232/) (2024)
- [MedSAM3: Delving into Segment Anything with Medical Concepts](https://mlsanity.com/paper/2511.19046/) (2025)
- [Towards Evaluating Explanations of Vision Transformers for Medical Imaging](https://mlsanity.com/paper/2304.06133/) (2023)
- [SAM2 for Image and Video Segmentation: A Comprehensive Survey](https://mlsanity.com/paper/2503.12781/) (2025)
- [Explainability of Vision Transformers: A Comprehensive Review and New Perspectives](https://mlsanity.com/paper/2311.06786/) (2023)
- [UniBiomed: A Universal Foundation Model for Grounded Biomedical Image Interpretation](https://mlsanity.com/paper/2504.21336/) (2025)
- [Federated Learning for Medical Image Classification: A Comprehensive Benchmark](https://mlsanity.com/paper/2504.05238/) (2025)
- [TIER: Text-Image Entropy Regularization for CLIP-style models](https://mlsanity.com/paper/2212.06710/) (2022)
- [BIO-CXRNET: A Robust Multimodal Stacking Machine Learning Technique for Mortality Risk Prediction of COVID-19 Patients using Chest X-Ray Images and Clinical Data](https://mlsanity.com/paper/2206.07595/) (2022)
- [Is Visual in-Context Learning for Compositional Medical Tasks within Reach?](https://mlsanity.com/paper/2507.00868/) (2025)
- [CLIP-SVD: Efficient and Interpretable Vision-Language Adaptation via Singular Values](https://mlsanity.com/paper/2509.03740/) (2025)
- [Self-Calibrated Consistency can Fight Back for Adversarial Robustness in Vision-Language Models](https://mlsanity.com/paper/2510.22785/) (2025)
- [vMFCoOp: Towards Equilibrium on a Unified Hyperspherical Manifold for Prompting Biomedical VLMs](https://mlsanity.com/paper/2511.09540/) (2025)
- [Towards Clinical Practice: Design and Implementation of Convolutional Neural Network-Based Assistive Diagnosis System for COVID-19 Case Detection from Chest X-Ray Images](https://mlsanity.com/paper/2203.10596/) (2022)
- [Explanations of Classifiers Enhance Medical Image Segmentation via End-to-end Pre-training](https://mlsanity.com/paper/2401.08469/) (2024)
- [Self-DenseMobileNet: A Robust Framework for Lung Nodule Classification using Self-ONN and Stacking-based Meta-Classifier](https://mlsanity.com/paper/2410.12584/) (2024)
- [MedRCube: A Multidimensional Framework for Fine-Grained and In-Depth Evaluation of MLLMs in Medical Imaging](https://mlsanity.com/paper/2604.13756/) (2026)
- [Localized Shortcut Removal](https://mlsanity.com/paper/2211.15510/) (2022)
- [Semi-Supervised Medical Image Segmentation via Knowledge Mining from Large Models](https://mlsanity.com/paper/2503.06816/) (2025)
- [A Comprehensive Study on Medical Image Segmentation using Deep Neural Networks](https://mlsanity.com/paper/2506.04121/) (2025)
- [Invisible Yet Detected: PelFANet with Attention-Guided Anatomical Fusion for Pelvic Fracture Diagnosis](https://mlsanity.com/paper/2509.13873/) (2025)
- [Similarity-as-Evidence: Calibrating Overconfident VLMs for Interpretable and Label-Efficient Medical Active Learning](https://mlsanity.com/paper/2602.18867/) (2026)
- [Adversarial Fragility and Language Vulnerability in Clinical AI: A Systematic Audit of Diagnostic Collapse Under Imperceptible Perturbations and Cross-Lingual Drift in Low-Resource Healthcare Settings](https://mlsanity.com/paper/2605.16993/) (2026)
- [MedFG-VQA: Low-Frequency Memory and Graph Attention for Lightweight Medical VQA](https://mlsanity.com/paper/2608.26848/) (2026)
- [MS-DCANet: A Novel Segmentation Network For Multi-Modality COVID-19 Medical Images](https://mlsanity.com/paper/2210.12361/) (2022)
- [PCDAL: A Perturbation Consistency-Driven Active Learning Approach for Medical Image Segmentation and Classification](https://mlsanity.com/paper/2306.16918/) (2023)
- [Deep learning in computed tomography pulmonary angiography imaging: a dual-pronged approach for pulmonary embolism detection](https://mlsanity.com/paper/2311.05197/) (2023)
- [InfLocNet: Enhanced Lung Infection Localization and Disease Detection from Chest X-Ray Images Using Lightweight Deep Learning](https://mlsanity.com/paper/2408.06459/) (2024)

## Abstract (arXiv)

Coronavirus disease 2019 (COVID-19) has been the main agenda of the whole world, since it came into sight in December 2019 as it has significantly affected the world economy and healthcare system. Given the effects of COVID-19 on pulmonary tissues, chest radiographic imaging has become a necessity for screening and monitoring the disease. Numerous studies have proposed Deep Learning approaches for the automatic diagnosis of COVID-19. Although these methods achieved astonishing performance in detection, they have used limited chest X-ray (CXR) repositories for evaluation, usually with a few hundred COVID-19 CXR images only. Thus, such data scarcity prevents reliable evaluation with the potential of overfitting. In addition, most studies showed no or limited capability in infection localization and severity grading of COVID-19 pneumonia. In this study, we address this urgent need by proposing a systematic and unified approach for lung segmentation and COVID-19 localization with infection quantification from CXR images. To accomplish this, we have constructed the largest benchmark dataset with 33,920 CXR images, including 11,956 COVID-19 samples, where the annotation of ground-truth lung segmentation masks is performed on CXRs by a novel human-machine collaborative approach. An extensive set of experiments was performed using the state-of-the-art segmentation networks, U-Net, U-Net++, and Feature Pyramid Networks (FPN). The developed network, after an extensive iterative process, reached a superior performance for lung region segmentation with Intersection over Union (IoU) of 96.11% and Dice Similarity Coefficient (DSC) of 97.99%. Furthermore, COVID-19 infections of various shapes and types were reliably localized with 83.05% IoU and 88.21% DSC. Finally, the proposed approach has achieved an outstanding COVID-19 detection performance with both sensitivity and specificity values above 99%.