ArticleIEEE access : practical innovations, open solutions2020
Uncertainty Assisted Robust Tuberculosis Identification With Bayesian Convolutional Neural Networks.
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 20 papers, 4 of them syntheses that pooled it.
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Who cites it
20 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
- A Lightweight Hybrid Deep Learning Model for Tuberculosis Detection from Chest X-Rays.Diagnostics (Basel, Switzerland) · 2025Article
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- Deep Learning-Based Classification and Semantic Segmentation of Lung Tuberculosis Lesions in Chest X-ray Images.Diagnostics (Basel, Switzerland) · 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
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- Robust Motor Imagery Tasks Classification Approach Using Bayesian Neural Network.Sensors (Basel, Switzerland) · 2023Article
- AI-based radiodiagnosis using chest X-rays: A review.Frontiers in big data · 2023Review
- Analyzing Transfer Learning of Vision Transformers for Interpreting Chest Radiography.Journal of digital imaging · 2022Article
- Ensemble Technique Coupled with Deep Transfer Learning Framework for Automatic Detection of Tuberculosis from Chest X-ray Radiographs.Healthcare (Basel, Switzerland) · 2022Article
- Recent Advances in Video Analytics for Rail Network Surveillance for Security, Trespass and Suicide Prevention-A Survey.Sensors (Basel, Switzerland) · 2022Article
- Predicting diarrhoea outbreaks with climate change.PloS one · 2022Article
- Monte Carlo Dropout for Uncertainty Estimation and Motor Imagery Classification.Sensors (Basel, Switzerland) · 2021Article
- 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
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9 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Tuberculosis (TB) is an infectious disease that can lead towards death if left untreated. TB detection involves extraction of complex TB manifestation features such as lung cavity, air space consolidation, endobronchial spread, and pleural effusions from chest x-rays (CXRs). Deep learning based approach named convolutional neural network (CNN) has the ability to learn complex features from CXR images. The main problem is that CNN does not consider uncertainty to classify CXRs using softmax layer. It lacks in presenting the true probability of CXRs by differentiating confusing cases during TB detection. This paper presents the solution for TB identification by using Bayesian-based convolutional neural network (B-CNN). It deals with the uncertain cases that have low discernibility among the TB and non-TB manifested CXRs. The proposed TB identification methodology based on B-CNN is evaluated on two TB benchmark datasets, i.e., Montgomery and Shenzhen. For training and testing of proposed scheme we have utilized Google Colab platform which provides NVidia Tesla K80 with 12 GB of VRAM, single core of 2.3 GHz Xeon Processor, 12 GB RAM and 320 GB of disk. B-CNN achieves 96.42% and 86.46% accuracy on both dataset, respectively as compared to the state-of-the-art machine learning and CNN approaches. Moreover, B-CNN validates its results by filtering the CXRs as confusion cases where the variance of B-CNN predicted outputs is more than a certain threshold. Results prove the supremacy of B-CNN for the identification of TB and non-TB sample CXRs as compared to counterparts in terms of accuracy, variance in the predicted probabilities and model uncertainty.
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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.