# COVID-ResNet: A Deep Learning Framework for Screening of COVID19 from Radiographs

arXiv: https://arxiv.org/abs/2003.14395 · HTML: https://mlsanity.com/paper/2003.14395/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Mar 2020.

## Built on by (most cited first, 28 of 28)

- [The Internet of Federated Things (IoFT): A Vision for the Future and In-depth Survey of Data-driven Approaches for Federated Learning](https://mlsanity.com/paper/2111.05326/) (2021)
- [COVID-Net CXR-2: An Enhanced Deep Convolutional Neural Network Design for Detection of COVID-19 Cases from Chest X-ray Images](https://mlsanity.com/paper/2105.06640/) (2021)
- [Backbones-Review: Feature Extraction Networks for Deep Learning and Deep Reinforcement Learning Approaches](https://mlsanity.com/paper/2206.08016/) (2022)
- [Fusion of convolution neural network, support vector machine and Sobel filter for accurate detection of COVID-19 patients using X-ray images](https://mlsanity.com/paper/2102.06883/) (2021)
- [UncertaintyFuseNet: Robust Uncertainty-aware Hierarchical Feature Fusion Model with Ensemble Monte Carlo Dropout for COVID-19 Detection](https://mlsanity.com/paper/2105.08590/) (2021)
- [The Prominence of Artificial Intelligence in COVID-19](https://mlsanity.com/paper/2111.09537/) (2021)
- [Efficient Backdoor Attacks for Deep Neural Networks in Real-world Scenarios](https://mlsanity.com/paper/2306.08386/) (2023)
- [Unconstrained Face-Mask & Face-Hand Datasets: Building a Computer Vision System to Help Prevent the Transmission of COVID-19](https://mlsanity.com/paper/2103.08773/) (2021)
- [Classification of COVID-19 on chest X-Ray images using Deep Learning model with Histogram Equalization and Lungs Segmentation](https://mlsanity.com/paper/2112.02478/) (2021)
- [OpenMedIA: Open-Source Medical Image Analysis Toolbox and Benchmark under Heterogeneous AI Computing Platforms](https://mlsanity.com/paper/2208.05616/) (2022)
- [Deep Learning Models May Spuriously Classify Covid-19 from X-ray Images Based on Confounders](https://mlsanity.com/paper/2102.04300/) (2021)
- [Synthetic COVID-19 Chest X-ray Dataset for Computer-Aided Diagnosis](https://mlsanity.com/paper/2106.09759/) (2021)
- [MobileCaps: A Lightweight Model for Screening and Severity Analysis of COVID-19 Chest X-Ray Images](https://mlsanity.com/paper/2108.08775/) (2021)
- [Predicting Visual Improvement after Macular Hole Surgery: a Cautionary Tale on Deep Learning with Very Limited Data](https://mlsanity.com/paper/2109.09463/) (2021)
- [Multi-Label Classification of Thoracic Diseases using Dense Convolutional Network on Chest Radiographs](https://mlsanity.com/paper/2202.03583/) (2022)
- [Adversarial Learning-based Stance Classifier for COVID-19-related Health Policies](https://mlsanity.com/paper/2209.04631/) (2022)
- [Fitness Dependent Optimizer with Neural Networks for COVID-19 patients](https://mlsanity.com/paper/2302.02986/) (2023)
- [COVID-19 Diagnosis: ULGFBP-ResNet51 approach on the CT and the Chest X-ray Images Classification](https://mlsanity.com/paper/2312.12876/) (2023)
- [AC-CovidNet: Attention Guided Contrastive CNN for Recognition of Covid-19 in Chest X-Ray Images](https://mlsanity.com/paper/2105.10239/) (2021)
- [A Data Augmented Approach to Transfer Learning for Covid-19 Detection](https://mlsanity.com/paper/2108.02870/) (2021)
- [COVID-19 Detection through Deep Feature Extraction](https://mlsanity.com/paper/2111.10762/) (2021)
- [A Deep Learning Approach for the Detection of COVID-19 from Chest X-Ray Images using Convolutional Neural Networks](https://mlsanity.com/paper/2201.09952/) (2022)
- [Biologically inspired deep residual networks for computer vision applications](https://mlsanity.com/paper/2205.02551/) (2022)
- [Towards Trustworthy Healthcare AI: Attention-Based Feature Learning for COVID-19 Screening With Chest Radiography](https://mlsanity.com/paper/2207.09312/) (2022)
- [Comparative Analysis of State-of-the-Art Deep Learning Models for Detecting COVID-19 Lung Infection from Chest X-Ray Images](https://mlsanity.com/paper/2208.01637/) (2022)
- [Inaccessible Neural Language Models Could Reinvigorate Linguistic Nativism](https://mlsanity.com/paper/2301.05272/) (2023)
- [Detection of COVID19 in Chest X-Ray Images Using Transfer Learning](https://mlsanity.com/paper/2304.04161/) (2023)
- [CoVScreen: Pitfalls and recommendations for screening COVID-19 using Chest X-rays](https://mlsanity.com/paper/2405.07674/) (2024)

## Abstract (arXiv)

In the last few months, the novel COVID19 pandemic has spread all over the world. Due to its easy transmission, developing techniques to accurately and easily identify the presence of COVID19 and distinguish it from other forms of flu and pneumonia is crucial. Recent research has shown that the chest Xrays of patients suffering from COVID19 depicts certain abnormalities in the radiography. However, those approaches are closed source and not made available to the research community for re-producibility and gaining deeper insight. The goal of this work is to build open source and open access datasets and present an accurate Convolutional Neural Network framework for differentiating COVID19 cases from other pneumonia cases. Our work utilizes state of the art training techniques including progressive resizing, cyclical learning rate finding and discriminative learning rates to training fast and accurate residual neural networks. Using these techniques, we showed the state of the art results on the open-access COVID-19 dataset. This work presents a 3-step technique to fine-tune a pre-trained ResNet-50 architecture to improve model performance and reduce training time. We call it COVIDResNet. This is achieved through progressively re-sizing of input images to 128x128x3, 224x224x3, and 229x229x3 pixels and fine-tuning the network at each stage. This approach along with the automatic learning rate selection enabled us to achieve the state of the art accuracy of 96.23% (on all the classes) on the COVIDx dataset with only 41 epochs. This work presented a computationally efficient and highly accurate model for multi-class classification of three different infection types from along with Normal individuals. This model can help in the early screening of COVID19 cases and help reduce the burden on healthcare systems.