ArticlePhysical and engineering sciences in medicine2021
Ensemble learning based automatic detection of tuberculosis in chest X-ray images using hybrid feature descriptors.
Article in Physical and engineering sciences in medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers, 4 of them syntheses that pooled it.
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Who cites it
42 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Diagnostic Performance of Artificial Intelligence-Based Methods for Tuberculosis Detection: Systematic Review.Journal of medical Internet research · 2025Pooled it
- Machine and Deep Learning for Tuberculosis Detection on Chest X-Rays: Systematic Literature Review.Journal of medical Internet research · 2023Pooled 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
- Ensemble deep learning architectures for detecting pulmonary tuberculosis in chest X-rays.Scientific reports · 2026Article
- Review
- Adaptive segmentation for accurate detection of tuberculosis disorder using multiscale residual densenet with attention mechanism by analyzing the chest X-ray images.Journal of clinical tuberculosis and other mycobacterial diseases · 2025Article
- Enhanced tuberculosis detection using Vision Transformers and explainable AI with a Grad-CAM approach on chest X-rays.BMC medical imaging · 2025Article
- Fully automated coronary artery calcium score and risk categorization from chest CT using deep learning and multiorgan segmentation: A validation study from National Lung Screening Trial (NLST).International journal of cardiology. Heart & vasculature · 2025Article
- Advanced federated ensemble internet of learning approach for cloud based medical healthcare monitoring system.Scientific reports · 2024Article
- Pattern classification of interstitial lung diseases from computed tomography images using a ResNet-based network with a split-transform-merge strategy and split attention.Physical and engineering sciences in medicine · 2024Article
- TSSG-CNN: A Tuberculosis Semantic Segmentation-Guided Model for Detecting and Diagnosis Using the Adaptive Convolutional Neural Network.Diagnostics (Basel, Switzerland) · 2024Article
- YOLOv8's advancements in tuberculosis identification from chest images.Frontiers in big data · 2024Article
- MENet: A Mitscherlich function based ensemble of CNN models to classify lung cancer using CT scans.PloS one · 2024Article
- Article
- Early diagnosis and meta-agnostic model visualization of tuberculosis based on radiography images.Scientific reports · 2023Article
- Parallel CNN-ELM: A multiclass classification of chest X-ray images to identify seventeen lung diseases including COVID-19.Expert systems with applications · 2023Article
- Improving diagnosis accuracy with an intelligent image retrieval system for lung pathologies detection: a features extractor approach.Scientific reports · 2023Article
- Detection of incomplete atypical femoral fracture on anteroposterior radiographs via explainable artificial intelligence.Scientific reports · 2023Article
- Detection of various lung diseases including COVID-19 using extreme learning machine algorithm based on the features extracted from a lightweight CNN architecture.Biocybernetics and biomedical engineering · 2023Article
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3 authors.
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Abstract
Tuberculosis (TB) remains one of the major health problems in modern times with a high mortality rate. While efforts are being made to make early diagnosis accessible and more reliable in high burden TB countries, digital chest radiography has become a popular source for this purpose. However, the screening process requires expert radiologists which may be a potential barrier in developing countries. A fully automatic computer-aided diagnosis system can reduce the need of trained personnel for early diagnosis of TB using chest X-ray images. In this paper, we have proposed a novel TB detection technique that combines hand-crafted features with deep features (convolutional neural network-based) through Ensemble Learning. Handcrafted features were extracted via Gabor Filter and deep features were extracted via pre-trained deep learning models. Two publicly available datasets namely (i) Montgomery and (ii) Shenzhen were used to evaluate the proposed system. The proposed methodology was validated with a k-fold cross-validation scheme. The area under receiver operating characteristics curves of 0.99 and 0.97 were achieved for Shenzhen and Montgomery datasets respectively which shows the superiority of the proposed scheme.
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