Article detail · 2018
A novel stacked generalization of models for improved TB detection in chest radiographs
- Year
- 2018
- Type
- conference-paper
Abstract
OpenAlex · English
Chest x-ray (CXR) analysis is a common part of the protocol for confirming active pulmonary Tuberculosis (TB). However, many TB endemic regions are severely resource constrained in radiological services impairing timely detection and treatment. Computer-aided diagnosis (CADx) tools can supplement decision-making while simultaneously addressing the gap in expert radiological interpretation during mobile field screening. These tools use hand-engineered and/or convolutional neural networks (CNN) computed image features. CNN, a class of deep learning (DL) models, has gained research prominence in visual recognition. It has been shown that Ensemble learning has an inherent advantage of constructing non-linear decision making functions and improve visual recognition. We create a stacking of classifiers with hand-engineered and CNN features toward improving TB detection in CXRs. The results obtained are highly promising and superior to the state-of-the-art.
Topics
Citations
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80 citations
OpenAlex cited_by_count (cache / database)
8 publications in the local catalog that cite this work (OpenAlex reference match; not the full global list).
- A voting-based ensemble deep learning method focusing on image augmentation and preprocessing variations for tuberculosis detection 2021
- Deep Learning for Grading Cardiomegaly Severity in Chest X-Rays: An Investigation 2018
- Spatial Pyramid Pooling in Deep Convolutional Networks for Automatic Tuberculosis Diagnosis 2020
- A Deep Learning-Based Framework for Uncertainty Quantification in Medical Imaging Using the DropWeak Technique: An Empirical Study with Baresnet 2023
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