ArticleBMC medical informatics and decision making2024
Optimization of vision transformer-based detection of lung diseases from chest X-ray images.
Article in BMC medical informatics and decision making, 2024. 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 comparative analysis of deep learning models for disease classification in multi-organ histopathological images.Scientific reports · 2026Article
- LungDxFormer: a transformer-CNN hybrid model with dynamic spatial attention for accurate lung cancer detection and classification.Scientific reports · 2026Article
- Construction and validation of high altitude pulmonary edema prediction models based on deep learning and quantitative analysis of X-ray images.BMC medical imaging · 2026Article
- Automated Shoulder Radiograph Quality Review to Support Efficient Workflow in the Emergency Department: The SQUIRE Deep Learning Ensemble.Journal of imaging informatics in medicine · 2026Article
- Class-attention pooling and token sparsity based vision transformers for chest X-ray interpretation.Scientific reports · 2026Article
- Anatomy-Guided Hybrid CNN-ViT Model with Neuro-Symbolic Reasoning for Early Diagnosis of Thoracic Diseases Multilabel.Diagnostics (Basel, Switzerland) · 2026Article
- Evaluation of large language models in a pulmonology outpatient clinic using structured clinical data and chest radiographs: a single-center prospective observational study.Frontiers in medicine · 2026Article
- A monotonic multi-expert Vision Transformer for clinically reliable chest X-ray classification.Frontiers in medicine · 2026Article
- Using swin UNETR deep model for automated detection of alveolar bone fenestration/dehiscence in CBCT.Frontiers in bioengineering and biotechnology · 2026Article
- [Image classification of osteoarthritis based on improved shifted windows transformer and graph convolutional networks].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2025Article
- A ubiquitous and interoperable deep learning model for automatic detection of pleomorphic gastroesophageal lesions.Scientific reports · 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
- A Deep Convolutional Neural Network Model for Lung Disease Detection Using Chest X-Ray Imaging.Pulmonary medicine · 2025Article
- Secure pulmonary diagnosis using transformer-based approach to X-ray classification with KL divergence optimization.Frontiers in medicine · 2025Article
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3 authors.
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Abstract
backgroundRecent advances in Vision Transformer (ViT)-based deep learning have significantly improved the accuracy of lung disease prediction from chest X-ray images. However, limited research exists on comparing the effectiveness of different optimizers for lung disease prediction within ViT models. This study aims to systematically evaluate and compare the performance of various optimization methods for ViT-based models in predicting lung diseases from chest X-ray images.
methodsThis study utilized a chest X-ray image dataset comprising 19,003 images containing both normal cases and six lung diseases: COVID-19, Viral Pneumonia, Bacterial Pneumonia, Middle East Respiratory Syndrome (MERS), Severe Acute Respiratory Syndrome (SARS), and Tuberculosis. Each ViT model (ViT, FastViT, and CrossViT) was individually trained with each optimization method (Adam, AdamW, NAdam, RAdam, SGDW, and Momentum) to assess their performance in lung disease prediction.
resultsWhen tested with ViT on the dataset with balanced-sample sized classes, RAdam demonstrated superior accuracy compared to other optimizers, achieving 95.87%. In the dataset with imbalanced sample size, FastViT with NAdam achieved the best performance with an accuracy of 97.63%.
conclusionsWe provide comprehensive optimization strategies for developing ViT-based model architectures, which can enhance the performance of these models for lung disease prediction from chest X-ray images.
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