ArticleScientific reports2024
Efficient pneumonia detection using Vision Transformers on chest X-rays.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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25 citing papers in PubMed, 91 citations in OpenAlex.
- Adaptive collaborative feature fusion and shape-aware optimization for multi-scale chest lesion detection.Scientific reports · 2026Article
- Analysis of Models to Estimate Morbidity Rates of Respiratory Diseases Through Deep Learning.Tropical medicine & international health : TM & IH · 2026Article
- In silico augmentation strategies for enhanced machine learning performance in fracture recognition.Scientific reports · 2026Article
- Ensemble localized patch residual convolution neural networks for pneumonia detection using chest X-ray.BMC medical informatics and decision making · 2026Article
- Uncertainty-Aware adaptive neuro-fuzzy transformer framework for robust multi-center lung disease classification.Scientific reports · 2026Article
- Development and evaluation of a multistage transfer learning framework for robust medical image analysis.Scientific reports · 2026Article
- Class-attention pooling and token sparsity based vision transformers for chest X-ray interpretation.Scientific reports · 2026Article
- Semi-supervised multi-class pneumonia classification using a CNN-cascade forest framework.Scientific reports · 2026Article
- A monotonic multi-expert Vision Transformer for clinically reliable chest X-ray classification.Frontiers in medicine · 2026Article
- Leveraging Vision Transformers for High-Precision Classification of Cancer Cell Cultures: A Comparative Study on MDA-MB-231 and PC3 Datasets.IEEE open journal of engineering in medicine and biology · 2026Article
- XceptRf-Net: A Novel Deep Learning and Machine Learning Approach for Pneumonia DiagnosisCurrent medical imaging · 2026Article
- Scaling up biomedical vision-language models: Fine-tuning, instruction tuning, and multi-modal learning.Journal of biomedical informatics · 2025Article
- A novel UNet-SegNet and vision transformer architectures for efficient segmentation and classification in medical imaging.Physical and engineering sciences in medicine · 2025Article
- Enhanced ResNet-50 with Multi-Feature Fusion for Robust Detection of Pneumonia in Chest X-Ray Images.Diagnostics (Basel, Switzerland) · 2025Article
- Deep learning approach for automated hMPV classification.Scientific reports · 2025Article
- Pneumonia Detection from Chest X-Ray Images Using Deep Learning and Transfer Learning for Imbalanced Datasets.Journal of imaging informatics in medicine · 2025Article
- Metaheuristic optimizers integrated with vision transformer model for severity detection and classification via multimodal COVID-19 images.Scientific reports · 2025Article
- An effective PO-RSNN and FZCIS based diabetes prediction and stroke analysis in the metaverse environment.Scientific reports · 2025Article
- A Distillation Approach to Transformer-Based Medical Image Classification with Limited Data.Diagnostics (Basel, Switzerland) · 2025Article
- A hybrid dense convolutional network and fuzzy inference system for pneumonia diagnosis with dynamic symptom tracking.PloS one · 2025Article
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Pneumonia is a widespread and acute respiratory infection that impacts people of all ages. Early detection and treatment of pneumonia are essential for avoiding complications and enhancing clinical results. We can reduce mortality, improve healthcare efficiency, and contribute to the global battle against a disease that has plagued humanity for centuries by devising and deploying effective detection methods. Detecting pneumonia is not only a medical necessity but also a humanitarian imperative and a technological frontier. Chest X-rays are a frequently used imaging modality for diagnosing pneumonia. This paper examines in detail a cutting-edge method for detecting pneumonia implemented on the Vision Transformer (ViT) architecture on a public dataset of chest X-rays available on Kaggle. To acquire global context and spatial relationships from chest X-ray images, the proposed framework deploys the ViT model, which integrates self-attention mechanisms and transformer architecture. According to our experimentation with the proposed Vision Transformer-based framework, it achieves a higher accuracy of 97.61%, sensitivity of 95%, and specificity of 98% in detecting pneumonia from chest X-rays. The ViT model is preferable for capturing global context, comprehending spatial relationships, and processing images that have different resolutions. The framework establishes its efficacy as a robust pneumonia detection solution by surpassing convolutional neural network (CNN) based architectures.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.