| _ | tuberculosis | _ |
|---|
| Automated identification of mycobacterium bacillus from sputum images for | tuberculosis | diagnosis |
| Automatic Detection of | tuberculosis | Bacilli from Microscopic Sputum Smear Images Using Faster R-CNN, Transfer Learning and Augmentation |
| Automatic Detection of | tuberculosis | in Chest Radiographs Using a Combination of Textural, Focal, and Shape Abnormality Analysis |
| Automatic Recognition of Mycobacterium | tuberculosis | Based on Active Shape Model |
| Automatic | tuberculosis | Detection Using Chest X-ray Analysis With Position Enhanced Structural Information |
| Automatic | tuberculosis | Screening Using Chest Radiographs |
| Computer Aided Diagnosis of Pleural Effusion in | tuberculosis | Chest Radiographs |
| deep learning-based x-ray imaging diagnosis system for classification of | tuberculosis | , COVID-19, and pneumonia traits using evolutionary algorithm, A |
| Diagnosing | tuberculosis | Using Deep Convolutional Neural Network |
| Enhanced Deep Learning Architecture for Classification of | tuberculosis | Types From CT Lung Images, An |
| Fractional crow search-based support vector neural network for patient classification and severity analysis of | tuberculosis | |
| Identification of | tuberculosis | bacteria based on shape and color |
| Identifying High-Risk Populations of | tuberculosis | Using Environmental Factors and GIS Based Multi-Criteria Decision Making Method |
| Improving | tuberculosis | (TB) Prediction using Synthetically Generated Computed Tomography (CT) Images |
| Inception-based Deep Learning Architecture for | tuberculosis | Screening using Chest X-rays |
| Novel coarse-to-fine dual scale technique for | tuberculosis | cavity detection in chest radiographs |
| Novel Multiple-Instance Learning-Based Approach to Computer-Aided Detection of | tuberculosis | on Chest X-Rays, A |
| On Combining Multiple-Instance Learning and Active Learning for Computer-Aided Detection of | tuberculosis | |
| Optimizing chest | tuberculosis | image classification with oversampling and transfer learning |
| Pre-trained Convolutional Neural Network for the Diagnosis of | tuberculosis | |
| Random forest-based | tuberculosis | bacteria classification in images of ZN-stained sputum smear samples |
| Rethinking Computer-Aided | tuberculosis | Diagnosis |
| Revisiting Computer-Aided | tuberculosis | Diagnosis |
| Segmentation and classification of | tuberculosis | bacilli from ZN-stained sputum smear images |
| semantic contour based segmentation of lungs from chest x-rays for the classification of | tuberculosis | using Naive Bayes classifier, A |
| Severity detection and infection level identification of | tuberculosis | using deep learning |
| Social Network Analysis of Spatial Human Mobility Behaviour In Infectious Disease Interaction: An Exploratory Evidence of | tuberculosis | In Malaysia |
| tuberculosis | Abnormality Detection in Chest X-rays: A Deep Learning Approach |
| tuberculosis | Analysis, Tuberculosis Bacilli |
| tuberculosis | Analysis, Tuberculosis Bacilli |
| TX-CNN: Detecting | tuberculosis | in chest X-ray images using convolutional neural network |
| Unsupervised contrastive unpaired image generation approach for improving | tuberculosis | screening using chest X-ray images |
32 for tuberculosis