ArticlePolish journal of radiology2022
Deep learning-based automatic detection of tuberculosis disease in chest X-ray images.
Article in Polish journal of radiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 1 of them a synthesis that pooled it.
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Who cites it
26 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Diagnostic Performance of Artificial Intelligence-Based Methods for Tuberculosis Detection: Systematic Review.Journal of medical Internet research · 2025Pooled it
- TB-SERS analyzer: Analysis tool for tuberculosis prediction based on Raman spectroscopy with machine learning and convolutional neural network.PLoS computational biology · 2026Article
- Respiratory Disease Detection: A Systematic Review of AI-Based Approaches, from Audio and Visual Unimodal Methods to Multimodal Integration.Diagnostics (Basel, Switzerland) · 2026Review
- An automated tuberculosis detection approach using deep learning and machine learning techniques from chest X-ray images: a step towards effective diagnosis.Polish journal of radiology · 2026Article
- Deep CNN-based Fully Automated Segmentation of Pelvic Multi-Organ on CT Images for Prostate Cancer Radiotherapy.Journal of biomedical physics & engineering · 2025Article
- Capsule Neural Networks with Bayesian Optimization for Pediatric Pneumonia Detection from Chest X-Ray Images.Journal of clinical medicine · 2025Article
- Classification Performance of Deep Learning Models for the Assessment of Vertical Dimension on Lateral Cephalometric Radiographs.Diagnostics (Basel, Switzerland) · 2025Article
- Tuberculosis detection using few shot learning.Scientific reports · 2025Article
- An adaptive convolution neural network model for tuberculosis detection and diagnosis using semantic segmentation.Polish journal of radiology · 2025Article
- Lung image segmentation with improved U-Net, V-Net and Seg-Net techniques.PeerJ. Computer science · 2025Article
- Convolutional Neural Network-Vision Transformer Architecture with Gated Control Mechanism and Multi-Scale Fusion for Enhanced Pulmonary Disease Classification.Diagnostics (Basel, Switzerland) · 2024Article
- Early detection of tuberculosis: a systematic review.Pneumonia (Nathan Qld.) · 2024Review
- XAI-FusionNet: Diabetic foot ulcer detection based on multi-scale feature fusion with explainable artificial intelligence.Heliyon · 2024Article
- Smartphone-based device for point-of-care diagnostics of pulmonary inflammation using convolutional neural networks (CNNs).Scientific reports · 2024Article
- Article
- Radiomics based Machine Learning Models for Classification of Prostate Cancer Grade Groups from Multi Parametric MRI Images.Journal of medical signals and sensors · 2024Article
- From Pixels to Pathology: Employing Computer Vision to Decode Chest Diseases in Medical Images.Cureus · 2023Review
- Uncertainty-Aware Convolutional Neural Network for Identifying Bilateral Opacities on Chest X-rays: A Tool to Aid Diagnosis of Acute Respiratory Distress Syndrome.Bioengineering (Basel, Switzerland) · 2023Article
- OView-AI Supporter for Classifying Pneumonia, Pneumothorax, Tuberculosis, Lung Cancer Chest X-ray Images Using Multi-Stage Superpixels Classification.Diagnostics (Basel, Switzerland) · 2023Article
- A Review of Recent Advances in Deep Learning Models for Chest Disease Detection Using Radiography.Diagnostics (Basel, Switzerland) · 2023Review
Corrections and comments
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5 authors.
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Abstract
Purpose: To train a convolutional neural network (CNN) model from scratch to automatically detect tuberculosis (TB) from chest X-ray (CXR) images and compare its performance with transfer learning based technique of different pre-trained CNNs. Material and methods: We used two publicly available datasets of postero-anterior chest radiographs, which are from Montgomery County, Maryland, and Shenzhen, China. A CNN (ConvNet) from scratch was trained to automatically detect TB on chest radiographs. Also, a CNN-based transfer learning approach using five different pre-trained models, including Inception_v3, Xception, ResNet50, VGG19, and VGG16 was utilized for classifying TB and normal cases from CXR images. The performance of models for testing datasets was evaluated using five performances metrics, including accuracy, sensitivity/recall, precision, area under curve (AUC), and F1-score. Results: All proposed models provided an acceptable accuracy for two-class classification. Our proposed CNN architecture (i.e., ConvNet) achieved 88.0% precision, 87.0% sensitivity, 87.0% F1-score, 87.0% accuracy, and AUC of 87.0%, which was slightly less than the pre-trained models. Among all models, Exception, ResNet50, and VGG16 provided the highest classification performance of automated TB classification with precision, sensitivity, F1-score, and AUC of 91.0%, and 90.0% accuracy. Conclusions: Our study presents a transfer learning approach with deep CNNs to automatically classify TB and normal cases from the chest radiographs. The classification accuracy, precision, sensitivity, and F1-score for the detection of TB were found to be more than 87.0% for all models used in the study. Exception, ResNet50, and VGG16 models outperformed other deep CNN models for the datasets with image augmentation methods.
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