# OSegNet: Operational Segmentation Network for COVID-19 Detection using Chest X-ray Images

arXiv: https://arxiv.org/abs/2202.10185 · HTML: https://mlsanity.com/paper/2202.10185/

## Key facts

- Cited by 48 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 29 references that are in the map.
- First posted on arXiv 21 Feb 2022.

## Builds on (most cited first, 21 of 21)

- [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)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [UNet++: A Nested U-Net Architecture for Medical Image Segmentation](https://mlsanity.com/paper/1807.10165/) (2018)
- [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)
- [Deep Layer Aggregation](https://mlsanity.com/paper/1707.06484/) (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: Automatic detection from X-Ray images utilizing Transfer Learning with Convolutional Neural Networks](https://mlsanity.com/paper/2003.11617/) (2020)
- [Automatic Detection of Coronavirus Disease (COVID-19) Using X-ray Images and Deep Convolutional Neural Networks](https://mlsanity.com/paper/2003.10849/) (2020)
- [BIMCV COVID-19+: a large annotated dataset of RX and CT images from COVID-19 patients](https://mlsanity.com/paper/2006.01174/) (2020)
- [COVID-19 Infection Localization and Severity Grading from Chest X-ray Images](https://mlsanity.com/paper/2103.07985/) (2021)
- [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)
- [Real-Time Glaucoma Detection from Digital Fundus Images using Self-ONNs](https://mlsanity.com/paper/2109.13604/) (2021)
- [PDCOVIDNet: A Parallel-Dilated Convolutional Neural Network Architecture for Detecting COVID-19 from Chest X-Ray Images](https://mlsanity.com/paper/2007.14777/) (2020)
- [COVID-19 Classification of X-ray Images Using Deep Neural Networks](https://mlsanity.com/paper/2010.01362/) (2020)
- [Self-Organized Variational Autoencoders (Self-VAE) for Learned Image Compression](https://mlsanity.com/paper/2105.12107/) (2021)
- [Deep Learning for Reliable Classification of COVID-19, MERS, and SARS from Chest X-Ray Images](https://mlsanity.com/paper/2005.11524/) (2020)
- [Reliable COVID-19 Detection Using Chest X-ray Images](https://mlsanity.com/paper/2101.12254/) (2021)
- [Self-Organized Residual Blocks for Image Super-Resolution](https://mlsanity.com/paper/2105.14926/) (2021)

## Built on by (most cited first, 30 of 48)

- [LViT: Language meets Vision Transformer in Medical Image Segmentation](https://mlsanity.com/paper/2206.14718/) (2022)
- [Ariadne's Thread:Using Text Prompts to Improve Segmentation of Infected Areas from Chest X-ray images](https://mlsanity.com/paper/2307.03942/) (2023)
- [Continual Self-supervised Learning: Towards Universal Multi-modal Medical Data Representation Learning](https://mlsanity.com/paper/2311.17597/) (2023)
- [Cross-Modal Conditioned Reconstruction for Language-guided Medical Image Segmentation](https://mlsanity.com/paper/2404.02845/) (2024)
- [Enhancing Label-efficient Medical Image Segmentation with Text-guided Diffusion Models](https://mlsanity.com/paper/2407.05323/) (2024)
- [SGSeg: Enabling Text-free Inference in Language-guided Segmentation of Chest X-rays via Self-guidance](https://mlsanity.com/paper/2409.04758/) (2024)
- [MedSG-Bench: A Benchmark for Medical Image Sequences Grounding](https://mlsanity.com/paper/2505.11852/) (2025)
- [Curriculum Prompting Foundation Models for Medical Image Segmentation](https://mlsanity.com/paper/2409.00695/) (2024)
- [Language-guided Medical Image Segmentation with Target-informed Multi-level Contrastive Alignments](https://mlsanity.com/paper/2412.13533/) (2024)
- [Alleviating Textual Reliance in Medical Language-guided Segmentation via Prototype-driven Semantic Approximation](https://mlsanity.com/paper/2507.11055/) (2025)
- [Instruction-Guided Lesion Segmentation for Chest X-rays with Automatically Generated Large-Scale Dataset](https://mlsanity.com/paper/2511.15186/) (2025)
- [Uncertainty-Aware Vision-Language Segmentation for Medical Imaging](https://mlsanity.com/paper/2602.14498/) (2026)
- [TextBraTS: Text-Guided Volumetric Brain Tumor Segmentation with Innovative Dataset Development and Fusion Module Exploration](https://mlsanity.com/paper/2506.16784/) (2025)
- [Frequency-domain Multi-modal Fusion for Language-guided Medical Image Segmentation](https://mlsanity.com/paper/2509.19719/) (2025)
- [MedCLIPSeg: Probabilistic Vision-Language Adaptation for Data-Efficient and Generalizable Medical Image Segmentation](https://mlsanity.com/paper/2602.20423/) (2026)
- [Multiscale Progressive Text Prompt Network for Medical Image Segmentation](https://mlsanity.com/paper/2307.00174/) (2023)
- [Federated Client-tailored Adapter for Medical Image Segmentation](https://mlsanity.com/paper/2504.18020/) (2025)
- [R2C-GAN: Restore-to-Classify Generative Adversarial Networks for Blind X-Ray Restoration and COVID-19 Classification](https://mlsanity.com/paper/2209.14770/) (2022)
- [Hyperparameter-Free Medical Image Synthesis for Sharing Data and Improving Site-Specific Segmentation](https://mlsanity.com/paper/2404.06240/) (2024)
- [Multi-dimensional Fusion and Consistency for Semi-supervised Medical Image Segmentation](https://mlsanity.com/paper/2309.06618/) (2023)
- [Language-guided Scale-aware MedSegmentor for Lesion Segmentation in Medical Imaging](https://mlsanity.com/paper/2408.17347/) (2024)
- [Text-guided multi-stage cross-perception network for medical image segmentation](https://mlsanity.com/paper/2506.07475/) (2025)
- [LEAF: Latent Diffusion with Efficient Encoder Distillation for Aligned Features in Medical Image Segmentation](https://mlsanity.com/paper/2507.18214/) (2025)
- [A Text-Image Fusion Method with Data Augmentation Capabilities for Referring Medical Image Segmentation](https://mlsanity.com/paper/2510.12482/) (2025)
- [InvCoSS: Inversion-driven Continual Self-supervised Learning in Medical Multi-modal Image Pre-training](https://mlsanity.com/paper/2512.19213/) (2025)
- [Localization-Infused Vision-Language Semantic Fusion for Text-Guided Medical Image Segmentation](https://mlsanity.com/paper/2607.16327/) (2026)
- [Dual-Domain Cross-Modal Decoding for Clinical Text-Guided Medical Image Segmentation](https://mlsanity.com/paper/2608.11335/) (2026)
- [AKGNet: Attribute Knowledge-Guided Unsupervised Lung-Infected Area Segmentation](https://mlsanity.com/paper/2404.11008/) (2024)
- [Quickly Tuning Foundation Models for Image Segmentation](https://mlsanity.com/paper/2508.17283/) (2025)
- [Medical Referring Image Segmentation via Next-Token Mask Prediction](https://mlsanity.com/paper/2511.05044/) (2025)

## Abstract (arXiv)

Coronavirus disease 2019 (COVID-19) has been diagnosed automatically using Machine Learning algorithms over chest X-ray (CXR) images. However, most of the earlier studies used Deep Learning models over scarce datasets bearing the risk of overfitting. Additionally, previous studies have revealed the fact that deep networks are not reliable for classification since their decisions may originate from irrelevant areas on the CXRs. Therefore, in this study, we propose Operational Segmentation Network (OSegNet) that performs detection by segmenting COVID-19 pneumonia for a reliable diagnosis. To address the data scarcity encountered in training and especially in evaluation, this study extends the largest COVID-19 CXR dataset: QaTa-COV19 with 121,378 CXRs including 9258 COVID-19 samples with their corresponding ground-truth segmentation masks that are publicly shared with the research community. Consequently, OSegNet has achieved a detection performance with the highest accuracy of 99.65% among the state-of-the-art deep models with 98.09% precision.