# Covid-19: Automatic detection from X-Ray images utilizing Transfer Learning with Convolutional Neural Networks

arXiv: https://arxiv.org/abs/2003.11617 · HTML: https://mlsanity.com/paper/2003.11617/

## Key facts

- Cited by 92 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Mar 2020.

## Built on by (most cited first, 30 of 92)

- [EnD: Entangling and Disentangling deep representations for bias correction](https://mlsanity.com/paper/2103.02023/) (2021)
- [CheXtransfer: Performance and Parameter Efficiency of ImageNet Models for Chest X-Ray Interpretation](https://mlsanity.com/paper/2101.06871/) (2021)
- [OSegNet: Operational Segmentation Network for COVID-19 Detection using Chest X-ray Images](https://mlsanity.com/paper/2202.10185/) (2022)
- [COVID-19 Infection Localization and Severity Grading from Chest X-ray Images](https://mlsanity.com/paper/2103.07985/) (2021)
- [Machine learning based disease diagnosis: A comprehensive review](https://mlsanity.com/paper/2112.15538/) (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)
- [Transfer Learning for Nonparametric Regression: Non-asymptotic Minimax Analysis and Adaptive Procedure](https://mlsanity.com/paper/2401.12272/) (2024)
- [Vision Transformer using Low-level Chest X-ray Feature Corpus for COVID-19 Diagnosis and Severity Quantification](https://mlsanity.com/paper/2104.07235/) (2021)
- [Hybrid quantum convolutional neural networks model for COVID-19 prediction using chest X-Ray images](https://mlsanity.com/paper/2102.06535/) (2021)
- [Self-supervised deep convolutional neural network for chest X-ray classification](https://mlsanity.com/paper/2103.03055/) (2021)
- [BCS-Net: Boundary, Context and Semantic for Automatic COVID-19 Lung Infection Segmentation from CT Images](https://mlsanity.com/paper/2207.08114/) (2022)
- [Backbones-Review: Feature Extraction Networks for Deep Learning and Deep Reinforcement Learning Approaches](https://mlsanity.com/paper/2206.08016/) (2022)
- [Comparison of fine-tuning strategies for transfer learning in medical image classification](https://mlsanity.com/paper/2406.10050/) (2024)
- [Uncertainty-Aware Semi-Supervised Method Using Large Unlabeled and Limited Labeled COVID-19 Data](https://mlsanity.com/paper/2102.06388/) (2021)
- [A Survey of Deep Learning Techniques for the Analysis of COVID-19 and their usability for Detecting Omicron](https://mlsanity.com/paper/2202.06372/) (2022)
- [Vision Transformer for COVID-19 CXR Diagnosis using Chest X-ray Feature Corpus](https://mlsanity.com/paper/2103.07055/) (2021)
- [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)
- [Cross-Site Severity Assessment of COVID-19 from CT Images via Domain Adaptation](https://mlsanity.com/paper/2109.03478/) (2021)
- [Randomly Initialized Convolutional Neural Network for the Recognition of COVID-19 using X-ray Images](https://mlsanity.com/paper/2105.08199/) (2021)
- [MedRDF: A Robust and Retrain-Less Diagnostic Framework for Medical Pretrained Models Against Adversarial Attack](https://mlsanity.com/paper/2111.14564/) (2021)
- [Automated detection of COVID-19 cases from chest X-ray images using deep neural network and XGBoost](https://mlsanity.com/paper/2109.02428/) (2021)
- [Interpretable CNN-Multilevel Attention Transformer for Rapid Recognition of Pneumonia from Chest X-Ray Images](https://mlsanity.com/paper/2210.16584/) (2022)
- [COVID-19 Modeling: A Review](https://mlsanity.com/paper/2104.12556/) (2021)
- [A novel framework based on deep learning and ANOVA feature selection method for diagnosis of COVID-19 cases from chest X-ray Images](https://mlsanity.com/paper/2110.06340/) (2021)
- [Optimising Chest X-Rays for Image Analysis by Identifying and Removing Confounding Factors](https://mlsanity.com/paper/2208.10320/) (2022)
- [Deep learning-based COVID-19 pneumonia classification using chest CT images: model generalizability](https://mlsanity.com/paper/2102.09616/) (2021)
- [Diagnosis of COVID-19 Using Machine Learning and Deep Learning: A review](https://mlsanity.com/paper/2110.14910/) (2021)
- [Robust Weakly Supervised Learning for COVID-19 Recognition Using Multi-Center CT Images](https://mlsanity.com/paper/2112.04984/) (2021)
- [An Incremental Learning Approach to Automatically Recognize Pulmonary Diseases from the Multi-vendor Chest Radiographs](https://mlsanity.com/paper/2201.02574/) (2022)
- [Diagnosis of COVID-19 Cases from Chest X-ray Images Using Deep Neural Network and LightGBM](https://mlsanity.com/paper/2203.14275/) (2022)

## Abstract (arXiv)

In this study, a dataset of X-Ray images from patients with common pneumonia, Covid-19, and normal incidents was utilized for the automatic detection of the Coronavirus. The aim of the study is to evaluate the performance of state-of-the-art Convolutional Neural Network architectures proposed over recent years for medical image classification. Specifically, the procedure called transfer learning was adopted. With transfer learning, the detection of various abnormalities in small medical image datasets is an achievable target, often yielding remarkable results. The dataset utilized in this experiment is a collection of 1427 X-Ray images. 224 images with confirmed Covid-19, 700 images with confirmed common pneumonia, and 504 images of normal conditions are included. The data was collected from the available X-Ray images on public medical repositories. With transfer learning, an overall accuracy of 97.82% in the detection of Covid-19 is achieved.