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COVIDX-Net: A Framework of Deep Learning Classifiers to Diagnose COVID-19 in X-Ray Images

arXiv 2003.11055 ↗First posted 24 Mar 2020eess.IV cs.CV cs.LGcited by 60

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Abstract (arXiv)

Background and Purpose: Coronaviruses (CoV) are perilous viruses that may cause Severe Acute Respiratory Syndrome (SARS-CoV), Middle East Respiratory Syndrome (MERS-CoV). The novel 2019 Coronavirus disease (COVID-19) was discovered as a novel disease pneumonia in the city of Wuhan, China at the end of 2019. Now, it becomes a Coronavirus outbreak around the world, the number of infected people and deaths are increasing rapidly every day according to the updated reports of the World Health Organization (WHO). Therefore, the aim of this article is to introduce a new deep learning framework; namely COVIDX-Net to assist radiologists to automatically diagnose COVID-19 in X-ray images. Materials and Methods: Due to the lack of public COVID-19 datasets, the study is validated on 50 Chest X-ray images with 25 confirmed positive COVID-19 cases. The COVIDX-Net includes seven different architectures of deep convolutional neural network models, such as modified Visual Geometry Group Network (VGG19) and the second version of Google MobileNet. Each deep neural network model is able to analyze the normalized intensities of the X-ray image to classify the patient status either negative or positive COVID-19 case. Results: Experiments and evaluation of the COVIDX-Net have been successfully done based on 80-20% of X-ray images for the model training and testing phases, respectively. The VGG19 and Dense Convolutional Network (DenseNet) models showed a good and similar performance of automated COVID-19 classification with f1-scores of 0.89 and 0.91 for normal and COVID-19, respectively. Conclusions: This study demonstrated the useful application of deep learning models to classify COVID-19 in X-ray images based on the proposed COVIDX-Net framework. Clinical studies are the next milestone of this research work.

Key facts. Cited by 60 papers in the map (data through 7 Oct 2026). First posted on arXiv 24 Mar 2020.

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Citing papers per year: 2021: 33; 2022: 11; 2023: 7; 2024: 6; 2025: 1; 2026: 2.

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  1. 2021Machine learning based disease diagnosis: A comprehensive reviewcited by 30
  2. 2024EVA-X: A Foundation Model for General Chest X-ray Analysis with Self-supervised Learningcited by 22
  3. 2021Vision Transformer using Low-level Chest X-ray Feature Corpus for COVID-19 Diagnosis and Severity Quantificationcited by 21
  4. 2022Backbones-Review: Feature Extraction Networks for Deep Learning and Deep Reinforcement Learning Approachescited by 15
  5. 2021Uncertainty-Aware Semi-Supervised Method Using Large Unlabeled and Limited Labeled COVID-19 Datacited by 13
  6. 2021Vision Transformer for COVID-19 CXR Diagnosis using Chest X-ray Feature Corpuscited by 12
  7. 2021Fusion of convolution neural network, support vector machine and Sobel filter for accurate detection of COVID-19 patients using X-ray imagescited by 10
  8. 2021Automated detection of COVID-19 cases from chest X-ray images using deep neural network and XGBoostcited by 8
  9. 2023Empowering COVID-19 Detection: Optimizing Performance Through Fine-Tuned EfficientNet Deep Learning Architecturecited by 8
  10. 2021COVID-19 Modeling: A Reviewcited by 7
  11. 2021The Prominence of Artificial Intelligence in COVID-19cited by 7
  12. 2021A novel framework based on deep learning and ANOVA feature selection method for diagnosis of COVID-19 cases from chest X-ray Imagescited by 6
38 more
  1. 2021Deep learning-based COVID-19 pneumonia classification using chest CT images: model generalizabilitycited by 5
  2. 2023Shortcut Detection with Variational Autoencoderscited by 5
  3. 2024An Explainable Non-local Network for COVID-19 Diagnosiscited by 5
  4. 2022Diagnosis of COVID-19 Cases from Chest X-ray Images Using Deep Neural Network and LightGBMcited by 4
  5. 2022AniWho : A Quick and Accurate Way to Classify Anime Character Faces in Imagescited by 4
  6. 2021Classification of COVID-19 on chest X-Ray images using Deep Learning model with Histogram Equalization and Lungs Segmentationcited by 3
  7. 2022Classification of COVID-19 in Chest X-ray Images Using Fusion of Deep Features and LightGBMcited by 3
  8. 2022Toward Robust Diagnosis: A Contour Attention Preserving Adversarial Defense for COVID-19 Detectioncited by 3
  9. 2021COVID-19 identification from volumetric chest CT scans using a progressively resized 3D-CNN incorporating segmentation, augmentation, and class-rebalancingcited by 2
  10. 2021Machine Learning and Deep Learning Methods for Building Intelligent Systems in Medicine and Drug Discovery: A Comprehensive Surveycited by 2
  11. 2023Case Studies on X-Ray Imaging, MRI and Nuclear Imagingcited by 2
  12. 2021Real-time Prediction for Mechanical Ventilation in COVID-19 Patients using A Multi-task Gaussian Process Multi-objective Self-attention Networkcited by 1
  13. 2021Deep Learning Models May Spuriously Classify Covid-19 from X-ray Images Based on Confounderscited by 1
  14. 2021Interpretable COVID-19 Chest X-Ray Classification via Orthogonality Constraintcited by 1
  15. 2021Probabilistic combination of eigenlungs-based classifiers for COVID-19 diagnosis in chest CT imagescited by 1
  16. 2021Detection of COVID-19 Disease using Deep Neural Networks with Ultrasound Imagingcited by 1
  17. 2021COVID-19 detection using deep convolutional neural networks and binary-differential-algorithm-based feature selection on X-ray imagescited by 1
  18. 2021Evolving Deep Convolutional Neural Network by Hybrid Sine-Cosine and Extreme Learning Machine for Real-time COVID19 Diagnosis from X-Ray Imagescited by 1
  19. 2021Systematic investigation into generalization of COVID-19 CT deep learning models with Gabor ensemble for lung involvement scoringcited by 1
  20. 2021Implementing a Detection System for COVID-19 based on Lung Ultrasound Imaging and Deep Learningcited by 1
  21. 2022Deterioration Prediction using Time-Series of Three Vital Signs and Current Clinical Features Amongst COVID-19 Patientscited by 1
  22. 2023Automated COVID-19 CT Image Classification using Multi-head Channel Attention in Deep CNNcited by 1
  23. 2023COVID-19 Diagnosis: ULGFBP-ResNet51 approach on the CT and the Chest X-ray Images Classificationcited by 1
  24. 2024COVID-19 Detection Based on Blood Test Parameters using Various Artificial Intelligence Methodscited by 1
  25. 2021Fused Deep Features Based Classification Framework for COVID-19 Classification with Optimized MLPcited by 0
  26. 2021DPN-SENet:A self-attention mechanism neural network for detection and diagnosis of COVID-19 from chest x-ray imagescited by 0
  27. 2021Tiled sparse coding in eigenspaces for the COVID-19 diagnosis in chest X-ray imagescited by 0
  28. 2021WisdomNet: Prognosis of COVID-19 with Slender Prospect of False Negative Cases and Vaticinating the Probability of Maturation to ARDS using Posteroanterior Chest X-Rayscited by 0
  29. 2021DuCN: Dual-children Network for Medical Diagnosis and Similar Case Recommendation towards COVID-19cited by 0
  30. 2021A Data Augmented Approach to Transfer Learning for Covid-19 Detectioncited by 0
  31. 2021Understanding Human Innate Immune System Dependencies using Graph Neural Networkscited by 0
  32. 2021Multimodal Detection of COVID-19 Symptoms using Deep Learning & Probability-based Weighting of Modescited by 0
  33. 2021Evaluation of Convolutional Neural Networks for COVID-19 Classification on Chest X-Rayscited by 0
  34. 2021COVID-19 Detection through Deep Feature Extractioncited by 0
  35. 2021Look at here : Utilizing supervision to attend subtle key regionscited by 0
  36. 2022Calibrated Bagging Deep Learning for Image Semantic Segmentation: A Case Study on COVID-19 Chest X-ray Imagecited by 0
  37. 2022Comparative Analysis of State-of-the-Art Deep Learning Models for Detecting COVID-19 Lung Infection from Chest X-Ray Imagescited by 0
  38. 2022Diagnosis of COVID-19 disease using CT scan images and pre-trained modelscited by 0

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