ArticleFrontiers in genetics2022
Detecting Tuberculosis-Consistent Findings in Lateral Chest X-Rays Using an Ensemble of CNNs and Vision Transformers.
Article in Frontiers in genetics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 2 of them syntheses that pooled it.
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12 citing papers in PubMed, 2 syntheses or guidelines pooled it, 31 citations in OpenAlex.
- Comparison of Vision Transformers and Convolutional Neural Networks in Medical Image Analysis: A Systematic Review.Journal of medical systems · 2024Pooled it
- Weak Localization of Radiographic Manifestations in Pulmonary Tuberculosis from Chest X-ray: A Systematic Review.Sensors (Basel, Switzerland) · 2023Pooled it
- Class-attention pooling and token sparsity based vision transformers for chest X-ray interpretation.Scientific reports · 2026Article
- Diagnostic Accuracy of an Offline CNN Framework Utilizing Multi-View Chest X-Rays for Screening 14 Co-Occurring Communicable and Non-Communicable Diseases.Diagnostics (Basel, Switzerland) · 2025Article
- Multi-view deep learning framework for the detection of chest X-rays compatible with pediatric pulmonary tuberculosis.Nature communications · 2025Article
- From Binary to Multi-Class Classification: A Two-Step Hybrid CNN-ViT Model for Chest Disease Classification Based on X-Ray Images.Diagnostics (Basel, Switzerland) · 2024Article
- AI-Driven Thoracic X-ray Diagnostics: Transformative Transfer Learning for Clinical Validation in Pulmonary Radiography.Journal of personalized medicine · 2024Article
- Review
- New trend in artificial intelligence-based assistive technology for thoracic imaging.La Radiologia medica · 2023Review
- A prospective multicenter clinical research study validating the effectiveness and safety of a chest X-ray-based pulmonary tuberculosis screening software JF CXR-1 built on a convolutional neural network algorithm.Frontiers in medicine · 2023Article
- Data Characterization for Reliable AI in Medicine.Recent trends in image processing and pattern recognition : 5th International Conference, RTIP2R 2022, Kingsville, TX, USA, December 01-02, 2022, revised selected papers. International Conference on Recent Trends in Image Processing and... · 2023Article
- The Application of Artificial Intelligence in the Diagnosis and Drug Resistance Prediction of Pulmonary Tuberculosis.Frontiers in medicine · 2022Review
Corrections and comments
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Authors and funding
4 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Research on detecting Tuberculosis (TB) findings on chest radiographs (or Chest X-rays: CXR) using convolutional neural networks (CNNs) has demonstrated superior performance due to the emergence of publicly available, large-scale datasets with expert annotations and availability of scalable computational resources. However, these studies use only the frontal CXR projections, i.e., the posterior-anterior (PA), and the anterior-posterior (AP) views for analysis and decision-making. Lateral CXRs which are heretofore not studied help detect clinically suspected pulmonary TB, particularly in children. Further, Vision Transformers (ViTs) with built-in self-attention mechanisms have recently emerged as a viable alternative to the traditional CNNs. Although ViTs demonstrated notable performance in several medical image analysis tasks, potential limitations exist in terms of performance and computational efficiency, between the CNN and ViT models, necessitating a comprehensive analysis to select appropriate models for the problem under study. This study aims to detect TB-consistent findings in lateral CXRs by constructing an ensemble of the CNN and ViT models. Several models are trained on lateral CXR data extracted from two large public collections to transfer modality-specific knowledge and fine-tune them for detecting findings consistent with TB. We observed that the weighted averaging ensemble of the predictions of CNN and ViT models using the optimal weights computed with the Sequential Least-Squares Quadratic Programming method delivered significantly superior performance (MCC: 0.8136, 95% confidence intervals (CI): 0.7394, 0.8878,
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