ArticleIEEE access : practical innovations, open solutions2020
Modality-specific deep learning model ensembles toward improving TB detection in chest radiographs.
Article in IEEE access : practical innovations, open solutions, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
44 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Advances in Deep Learning for Tuberculosis Screening using Chest X-rays: The Last 5 Years Review.Journal of medical systems · 2022Pooled it
- A Systematic Review of Deep Learning Techniques for Tuberculosis Detection From Chest Radiograph.Frontiers in medicine · 2022Pooled it
- Artificial Intelligence for Tuberculosis Screening and Detection: From Evidence to Policy and Implementation.Diagnostics (Basel, Switzerland) · 2026Review
- Ensemble deep learning architectures for detecting pulmonary tuberculosis in chest X-rays.Scientific reports · 2026Article
- GBWOEM: A Gradient-Based Weight Optimization Model for Improved Predictive Accuracy in Healthcare.F1000Research · 2025Article
- Convolutional Neural Network-Vision Transformer Architecture with Gated Control Mechanism and Multi-Scale Fusion for Enhanced Pulmonary Disease Classification.Diagnostics (Basel, Switzerland) · 2024Article
- Deep Learning-Based Classification and Semantic Segmentation of Lung Tuberculosis Lesions in Chest X-ray Images.Diagnostics (Basel, Switzerland) · 2024Article
- Deep transfer learning with fuzzy ensemble approach for the early detection of breast cancer.BMC medical imaging · 2024Article
- YOLOv8's advancements in tuberculosis identification from chest images.Frontiers in big data · 2024Article
- Review on chest pathogies detection systems using deep learning techniques.Artificial intelligence review · 2023Article
- Cross Dataset Analysis of Domain Shift in CXR Lung Region Detection.Diagnostics (Basel, Switzerland) · 2023Article
- Stacked ensemble deep learning for pancreas cancer classification using extreme gradient boosting.Frontiers in artificial intelligence · 2023Article
- AI-based radiodiagnosis using chest X-rays: A review.Frontiers in big data · 2023Review
- Automatic Detection of Tuberculosis Using VGG19 with Seagull-Algorithm.Life (Basel, Switzerland) · 2022Article
- Development of a multipotent diagnostic tool for chest X-rays by multi-object detection method.Scientific reports · 2022Article
- Editorial on Special Issue "Artificial Intelligence in Image-Based Screening, Diagnostics, and Clinical Care of Cardiopulmonary Diseases".Diagnostics (Basel, Switzerland) · 2022Article
- Computer-aided diagnostic for classifying chest X-ray images using deep ensemble learning.BMC medical imaging · 2022Article
- Deep Ensemble Learning for the Automatic Detection of Pneumoconiosis in Coal Worker's Chest X-ray Radiography.Journal of clinical medicine · 2022Article
- Detection and Visualisation of Pneumoconiosis Using an Ensemble of Multi-Dimensional Deep Features Learned from Chest X-rays.International journal of environmental research and public health · 2022Article
- COVID-19 chest X-ray detection through blending ensemble of CNN snapshots.Biomedical signal processing and control · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
The proposed study evaluates the efficacy of knowledge transfer gained through an ensemble of modality-specific deep learning models toward improving the state-of-the-art in Tuberculosis (TB) detection. A custom convolutional neural network (CNN) and selected popular pretrained CNNs are trained to learn modality-specific features from large-scale publicly available chest x-ray (CXR) collections including (i) RSNA dataset (normal = 8851, abnormal = 17833), (ii) Pediatric pneumonia dataset (normal = 1583, abnormal = 4273), and (iii) Indiana dataset (normal = 1726, abnormal = 2378). The knowledge acquired through modality-specific learning is transferred and fine-tuned for TB detection on the publicly available Shenzhen CXR collection (normal = 326, abnormal =336). The predictions of the best performing models are combined using different ensemble methods to demonstrate improved performance over any individual constituent model in classifying TB-infected and normal CXRs. The models are evaluated through cross-validation (n = 5) at the patient-level with an aim to prevent overfitting, improve robustness and generalization. It is observed that a stacked ensemble of the top-3 retrained models demonstrates promising performance (accuracy: 0.941; 95% confidence interval (CI): [0.899, 0.985], area under the curve (AUC): 0.995; 95% CI: [0.945, 1.00]). One-way ANOVA analyses show there are no statistically significant differences in accuracy (
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.