ArticleIEEE access : practical innovations, open solutions2022
Deep GRU-CNN Model for COVID-19 Detection From Chest X-Rays Data.
Article in IEEE access : practical innovations, open solutions, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 2 of them syntheses that pooled it.
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Who cites it
28 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Supervised and weakly supervised deep learning models for COVID-19 CT diagnosis: A systematic review.Computer methods and programs in biomedicine · 2022Pooled it
- Role of Artificial Intelligence in COVID-19 Detection.Sensors (Basel, Switzerland) · 2021Pooled it
- Spine-anchored active contour-based renal spatial prioritization for precision-constrained volumetric segmentation of bilateral nephric tumors via spine-guided 3D deep encoders in abdominopelvic tomographic data.Radiological physics and technology · 2026Article
- A 19-layer convolutional neural network for accurate COVID-19 detection in chest X-ray images: comparative analysis with pretrained networks.Scientific reports · 2026Article
- Review
- White blood cell classification using custom deep neural network and visualizing features of the images using heatmaps.Scientific reports · 2026Article
- Semi-supervised multi-class pneumonia classification using a CNN-cascade forest framework.Scientific reports · 2026Article
- LungGANDetectAI: a GAN-augmented and attention-guided deep learning framework for accurate and explainable lung cancer detection.Scientific reports · 2026Article
- Optimized CNN framework for malaria detection using Otsu thresholding-based image segmentation.Scientific reports · 2025Article
- Automated Computer-Assisted Diagnosis of Pleural Effusion in Chest X-Rays via Deep Learning.Diagnostics (Basel, Switzerland) · 2025Article
- DeepOptimalNet: optimized deep learning model for early diagnosis of pancreatic tumor classification in CT imaging.Abdominal radiology (New York) · 2025Article
- An optimized domain-specific shrimp detection architecture integrating conditional GAN and weighted ensemble learning.Scientific reports · 2025Article
- Enhanced tuberculosis detection using Vision Transformers and explainable AI with a Grad-CAM approach on chest X-rays.BMC medical imaging · 2025Article
- Lung cancer detection with machine learning classifiers with multi-attribute decision-making system and deep learning model.Scientific reports · 2025Article
- Deep Learning for Pneumonia Detection in Chest X-ray Images: A Comprehensive Survey.Journal of imaging · 2024Review
- COVID-19 detection from chest X-ray images using CLAHE-YCrCb, LBP, and machine learning algorithms.BMC bioinformatics · 2024Article
- Detection of COVID-19 using edge devices by a light-weight convolutional neural network from chest X-ray images.BMC medical imaging · 2024Article
- Challenges issues and future recommendations of deep learning techniques for SARS-CoV-2 detection utilising X-ray and CT images: a comprehensive review.PeerJ. Computer science · 2024Article
- Efficient prediction of anticancer peptides through deep learning.PeerJ. Computer science · 2024Article
- Classification of Parkinson's Disease in Patch-Based MRI of Substantia Nigra.Diagnostics (Basel, Switzerland) · 2023Article
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Authors and funding
9 authors.
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No grant is acknowledged in the PubMed record.
Abstract
In the current era, data is growing exponentially due to advancements in smart devices. Data scientists apply a variety of learning-based techniques to identify underlying patterns in the medical data to address various health-related issues. In this context, automated disease detection has now become a central concern in medical science. Such approaches can reduce the mortality rate through accurate and timely diagnosis. COVID-19 is a modern virus that has spread all over the world and is affecting millions of people. Many countries are facing a shortage of testing kits, vaccines, and other resources due to significant and rapid growth in cases. In order to accelerate the testing process, scientists around the world have sought to create novel methods for the detection of the virus. In this paper, we propose a hybrid deep learning model based on a convolutional neural network (CNN) and gated recurrent unit (GRU) to detect the viral disease from chest X-rays (CXRs). In the proposed model, a CNN is used to extract features, and a GRU is used as a classifier. The model has been trained on 424 CXR images with 3 classes (COVID-19, Pneumonia, and Normal). The proposed model achieves encouraging results of 0.96, 0.96, and 0.95 in terms of precision, recall, and f1-score, respectively. These findings indicate how deep learning can significantly contribute to the early detection of COVID-19 in patients through the analysis of X-ray scans. Such indications can pave the way to mitigate the impact of the disease. We believe that this model can be an effective tool for medical practitioners for early diagnosis.
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