ArticleBioengineering (Basel, Switzerland)2022
Automated Lung-Related Pneumonia and COVID-19 Detection Based on Novel Feature Extraction Framework and Vision Transformer Approaches Using Chest X-ray Images.
Article in Bioengineering (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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15 citing papers in PubMed.
- A monotonic multi-expert Vision Transformer for clinically reliable chest X-ray classification.Frontiers in medicine · 2026Article
- NextGen lung disease diagnosis with explainable artificial intelligence.Scientific reports · 2025Article
- Deep learning approaches for classification tasks in medical X-ray, MRI, and ultrasound images: a scoping review.BMC medical imaging · 2025Article
- Metaheuristic optimizers integrated with vision transformer model for severity detection and classification via multimodal COVID-19 images.Scientific reports · 2025Article
- Automated classification of chest X-rays: a deep learning approach with attention mechanisms.BMC medical imaging · 2025Article
- Convolutional Neural Network-Vision Transformer Architecture with Gated Control Mechanism and Multi-Scale Fusion for Enhanced Pulmonary Disease Classification.Diagnostics (Basel, Switzerland) · 2024Article
- Artificial Intelligence Diagnosing of Oral Lichen Planus: A Comparative Study.Bioengineering (Basel, Switzerland) · 2024Article
- Machine Learning for Biomedical Applications.Bioengineering (Basel, Switzerland) · 2024Article
- Evaluation of Denoising Performance of ResNet Deep Learning Model for Ultrasound Images Corresponding to Two Frequency Parameters.Bioengineering (Basel, Switzerland) · 2024Article
- Integrating Convolutional Neural Networks with Attention Mechanisms for Magnetic Resonance Imaging-Based Classification of Brain Tumors.Bioengineering (Basel, Switzerland) · 2024Article
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- Artificial Intelligence for Personalized Genetics and New Drug Development: Benefits and Cautions.Bioengineering (Basel, Switzerland) · 2023Article
- A Hybrid Stacked Restricted Boltzmann Machine with Sobel Directional Patterns for Melanoma Prediction in Colored Skin Images.Diagnostics (Basel, Switzerland) · 2023Article
- An Automatic Premature Ventricular Contraction Recognition System Based on Imbalanced Dataset and Pre-Trained Residual Network Using Transfer Learning on ECG Signal.Diagnostics (Basel, Switzerland) · 2022Article
- Efficient Transformer-Based Compressed Video Modeling via Informative Patch Selection.Sensors (Basel, Switzerland) · 2022Article
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10 authors.
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Abstract
According to research, classifiers and detectors are less accurate when images are blurry, have low contrast, or have other flaws which raise questions about the machine learning model's ability to recognize items effectively. The chest X-ray image has proven to be the preferred image modality for medical imaging as it contains more information about a patient. Its interpretation is quite difficult, nevertheless. The goal of this research is to construct a reliable deep-learning model capable of producing high classification accuracy on chest x-ray images for lung diseases. To enable a thorough study of the chest X-ray image, the suggested framework first derived richer features using an ensemble technique, then a global second-order pooling is applied to further derive higher global features of the images. Furthermore, the images are then separated into patches and position embedding before analyzing the patches individually via a vision transformer approach. The proposed model yielded 96.01% sensitivity, 96.20% precision, and 98.00% accuracy for the COVID-19 Radiography Dataset while achieving 97.84% accuracy, 96.76% sensitivity and 96.80% precision, for the Covid-ChestX-ray-15k dataset. The experimental findings reveal that the presented models outperform traditional deep learning models and other state-of-the-art approaches provided in the literature.
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