ArticleBiomedical signal processing and control2022
Deep learning-based approach for detecting COVID-19 in chest X-rays.
Article in Biomedical signal processing and control, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- ARTEMIS: An Explainable AI Framework for Multi-Class COVID-19 Diagnosis with a Newly Curated Dataset.Bioengineering (Basel, Switzerland) · 2026Article
- Efficient Cerebral Infarction Segmentation Using U-Net and U-Net3 + Models.Journal of imaging informatics in medicine · 2026Article
- An efficient dual path deep learning framework for COVID-19 classification using lung CT scans with explainable AI.Scientific reports · 2026Article
- Segmentation-Assisted Fusion-Based Classification for Automated CXR Image Analysis.Sensors (Basel, Switzerland) · 2025Article
- 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
- COVID‑19 detection from chest X-ray images using transfer learning.Scientific reports · 2024Article
- Auto-detection of the coronavirus disease by using deep convolutional neural networks and X-ray photographs.Scientific reports · 2024Article
- Optimized Xception Learning Model and XgBoost Classifier for Detection of Multiclass Chest Disease from X-ray Images.Diagnostics (Basel, Switzerland) · 2023Article
- Lightweight deep CNN-based models for early detection of COVID-19 patients from chest X-ray images.Expert systems with applications · 2023Article
- Article
- COVID-19 CT ground-glass opacity segmentation based on attention mechanism threshold.Biomedical signal processing and control · 2023Article
- Detection and classification of COVID-19 by using faster R-CNN and mask R-CNN on CT images.Neural computing & applications · 2023Article
- COVID-DSNet: A novel deep convolutional neural network for detection of coronavirus (SARS-CoV-2) cases from CT and Chest X-Ray images.Artificial intelligence in medicine · 2022Article
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1 author.
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Abstract
Today, 2019 Coronavirus (COVID-19) infections are a major health concern worldwide. Therefore, detecting COVID-19 in X-ray images is crucial for diagnosis, evaluation, and treatment. Furthermore, expressing diagnostic uncertainty in a report is a challenging duty but unavoidable task for radiologists. This study proposes a novel CNN (Convolutional Neural Network) model for automatic COVID-19 identification utilizing chest X-ray images. The proposed CNN model is designed to be a reliable diagnostic tool for two-class categorization (COVID and Normal). In addition to the proposed model, different architectures, including the pre-trained MobileNetv2 and ResNet50 models, are evaluated for this COVID-19 dataset (13,824 X-ray images) and our suggested model is compared to these existing COVID-19 detection algorithms in terms of accuracy. Experimental results show that our proposed model identifies patients with COVID-19 disease with 96.71 percent accuracy, 91.89 percent F1-score. Our proposed approach CNN's experimental results show that it outperforms the most advanced algorithms currently available. This model can assist clinicians in making informed judgments on how to diagnose COVID-19, as well as make test kits more accessible.
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