ArticleJournal of medical systems2018
Feature Selection for Automatic Tuberculosis Screening in Frontal Chest Radiographs.
Article in Journal of medical systems, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 3 of them syntheses that pooled it.
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Who cites it
28 citing papers in PubMed, 3 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
- Pooled it
- Multi-view deep learning framework for the detection of chest X-rays compatible with pediatric pulmonary tuberculosis.Nature communications · 2025Article
- A methodical exploration of imaging modalities from dataset to detection through machine learning paradigms in prominent lung disease diagnosis: a review.BMC medical imaging · 2024Review
- Review on chest pathogies detection systems using deep learning techniques.Artificial intelligence review · 2023Article
- Analyzing Overlaid Foreign Objects in Chest X-rays-Clinical Significance and Artificial Intelligence Tools.Healthcare (Basel, Switzerland) · 2023Review
- Distinguishing infectivity in patients with pulmonary tuberculosis using deep learning.Frontiers in public health · 2023Article
- CardioNet: Automatic Semantic Segmentation to Calculate the Cardiothoracic Ratio for Cardiomegaly and Other Chest Diseases.Journal of personalized medicine · 2022Article
- Deep features to detect pulmonary abnormalities in chest X-rays due to infectious diseaseX: Covid-19, pneumonia, and tuberculosis.Information sciences · 2022Article
- AI-Assisted Tuberculosis Detection and Classification from Chest X-Rays Using a Deep Learning Normalization-Free Network Model.Computational intelligence and neuroscience · 2022Article
- Early Diagnosis of Tuberculosis Using Deep Learning Approach for IOT Based Healthcare Applications.Computational intelligence and neuroscience · 2022Article
- TB-Net: A Tailored, Self-Attention Deep Convolutional Neural Network Design for Detection of Tuberculosis Cases From Chest X-Ray Images.Frontiers in artificial intelligence · 2022Article
- Ensemble learning based automatic detection of tuberculosis in chest X-ray images using hybrid feature descriptors.Physical and engineering sciences in medicine · 2021Article
- Coronavirus disease (COVID-19) detection in Chest X-Ray images using majority voting based classifier ensemble.Expert systems with applications · 2021Article
- Computer-aided diagnosis based on hand thermal, RGB images, and grip force using artificial intelligence as screening tool for rheumatoid arthritis in women.Medical & biological engineering & computing · 2021Article
- A voting-based ensemble deep learning method focusing on image augmentation and preprocessing variations for tuberculosis detection.Neural computing & applications · 2021Article
- Comprehensive Computer-Aided Decision Support Framework to Diagnose Tuberculosis From Chest X-Ray Images: Data Mining Study.JMIR medical informatics · 2020Article
- A Survey of Deep Learning for Lung Disease Detection on Medical Images: State-of-the-Art, Taxonomy, Issues and Future Directions.Journal of imaging · 2020Review
- Contour-based lung shape analysis in order to tuberculosis detection: modeling and feature description.Medical & biological engineering & computing · 2020Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
To detect pulmonary abnormalities such as Tuberculosis (TB), an automatic analysis and classification of chest radiographs can be used as a reliable alternative to more sophisticated and technologically demanding methods (e.g. culture or sputum smear analysis). In target areas like Kenya TB is highly prevalent and often co-occurring with HIV combined with low resources and limited medical assistance. In these regions an automatic screening system can provide a cost-effective solution for a large rural population. Our completely automatic TB screening system is processing the incoming CXRs (chest X-ray) by applying image preprocessing techniques to enhance the image quality followed by an adaptive segmentation based on model selection. The delineated lung regions are described by a multitude of image features. These characteristics are than optimized by a feature selection strategy to provide the best description for the classifier, which will later decide if the analyzed image is normal or abnormal. Our goal is to find the optimal feature set from a larger pool of generic image features, -used originally for problems such as object detection, image retrieval, etc. For performance evaluation measures such as under the curve (AUC) and accuracy (ACC) were considered. Using a neural network classifier on two publicly available data collections, -namely the Montgomery and the Shenzhen dataset, we achieved the maximum area under the curve and accuracy of 0.99 and 97.03%, respectively. Further, we compared our results with existing state-of-the-art systems and to radiologists' decision.
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Registered trials
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