ArticleComputers in biology and medicine2021
CovidXrayNet: Optimizing data augmentation and CNN hyperparameters for improved COVID-19 detection from CXR.
Article in Computers in biology and medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers, 1 of them a synthesis that pooled it.
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Who cites it
29 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Automated COVID-19 diagnosis and prognosis with medical imaging and who is publishing: a systematic review.Physical and engineering sciences in medicine · 2022Pooled it
- A Novel Method for 3D Lung Tumor Reconstruction Using Generative Models.Diagnostics (Basel, Switzerland) · 2024Article
- Effectiveness of data-augmentation on deep learning in evaluating rapid on-site cytopathology at endoscopic ultrasound-guided fine needle aspiration.Scientific reports · 2024Article
- Adaptive Mish activation and ranger optimizer-based SEA-ResNet50 model with explainable AI for multiclass classification of COVID-19 chest X-ray images.BMC medical imaging · 2024Article
- Machine learning-assisted substrate binding pocket engineering based on structural information.Briefings in bioinformatics · 2024Article
- Deep learning framework for rapid and accurate respiratory COVID-19 prediction using chest X-ray images.Journal of King Saud University. Computer and information sciences · 2023Article
- Perceptive SARS-CoV-2 End-To-End Ultrasound Video Classification through X3D and Key-Frames Selection.Bioengineering (Basel, Switzerland) · 2023Article
- Uncertain-CAM: Uncertainty-Based Ensemble Machine Voting for Improved COVID-19 CXR Classification and Explainability.Diagnostics (Basel, Switzerland) · 2023Article
- Detection of Covid-19 and other pneumonia cases from CT and X-ray chest images using deep learning based on feature reuse residual block and depthwise dilated convolutions neural network.Applied soft computing · 2023Article
- Application of Machine Learning and Deep Learning Techniques for COVID-19 Screening Using Radiological Imaging: A Comprehensive Review.SN computer science · 2023Article
- PSTCNN: Explainable COVID-19 diagnosis using PSO-guided self-tuning CNN.Biocell : official journal of the Sociedades Latinoamericanas de Microscopia Electronica ... et. al · 2023Article
- A MobileNet-based CNN model with a novel fine-tuning mechanism for COVID-19 infection detection.Soft computing · 2023Article
- Article
- Comparative study of convolutional neural network architectures for gastrointestinal lesions classification.PeerJ · 2023Article
- Computer-aided diagnosis using embedded ensemble deep learning for multiclass drug-resistant tuberculosis classification.Frontiers in medicine · 2023Article
- Deep Content Information Retrieval for COVID-19 Detection from Chromatic CT Scans.Arabian journal for science and engineering · 2023Article
- A Survey on Deep Learning in COVID-19 Diagnosis.Journal of imaging · 2022Review
- Automated Lung-Related Pneumonia and COVID-19 Detection Based on Novel Feature Extraction Framework and Vision Transformer Approaches Using Chest X-ray Images.Bioengineering (Basel, Switzerland) · 2022Article
- COVID-19 detection on Chest X-ray images: A comparison of CNN architectures and ensembles.Expert systems with applications · 2022Article
- A Comprehensive Performance Analysis of Transfer Learning Optimization in Visual Field Defect Classification.Diagnostics (Basel, Switzerland) · 2022Article
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4 authors.
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Abstract
To mitigate the spread of the current coronavirus disease 2019 (COVID-19) pandemic, it is crucial to have an effective screening of infected patients to be isolated and treated. Chest X-Ray (CXR) radiological imaging coupled with Artificial Intelligence (AI) applications, in particular Convolutional Neural Network (CNN), can speed the COVID-19 diagnostic process. In this paper, we optimize the data augmentation and the CNN hyperparameters for detecting COVID-19 from CXRs in terms of validation accuracy. This optimization increases the accuracy of the popular CNN architectures such as the Visual Geometry Group network (VGG-19) and the Residual Neural Network (ResNet-50), by 11.93% and 4.97%, respectively. We then proposed CovidXrayNet model that is based on EfficientNet-B0 and our optimization results. We evaluated CovidXrayNet on two datasets, including our generated balanced COVIDcxr dataset (960 CXRs) and the benchmark COVIDx dataset (15,496 CXRs). With only 30 epochs of training, CovidXrayNet achieves state-of-the-art accuracy of 95.82% on the COVIDx dataset in the three-class classification task (COVID-19, normal or pneumonia). The CovidXRayNet model, the COVIDcxr dataset, and several optimization experiments are publicly available at https://github.com/MaramMonshi/CovidXrayNet.
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