ArticleNeural computing & applications2021
A voting-based ensemble deep learning method focusing on image augmentation and preprocessing variations for tuberculosis detection.
Article in Neural computing & applications, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.
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22 citing papers in PubMed, 1 synthesis or guideline pooled it, 108 citations in OpenAlex.
- Diagnostic Performance of Artificial Intelligence-Based Methods for Tuberculosis Detection: Systematic Review.Journal of medical Internet research · 2025Pooled it
- Active and Inactive Tuberculosis Classification Using Convolutional Neural Networks with MLP-Mixer.Bioengineering (Basel, Switzerland) · 2025Article
- Deep Ensembling of Multiband Images for Earth Remote Sensing and Foramnifera Data.Sensors (Basel, Switzerland) · 2025Article
- Deep Learning Models for Anatomical Location Classification in Esophagogastroduodenoscopy Images and Videos: A Quantitative Evaluation with Clinical Data.Diagnostics (Basel, Switzerland) · 2024Article
- MetaWise: Combined Feature Selection and Weighting Method to Link the Serum Metabolome to Treatment Response and Survival in Glioblastoma.International journal of molecular sciences · 2024Article
- Explainable deep-neural-network supported scheme for tuberculosis detection from chest radiographs.BMC medical imaging · 2024Article
- Explainable artificial intelligence approaches for COVID-19 prognosis prediction using clinical markers.Scientific reports · 2024Article
- Article
- A robust ensemble deep learning framework for accurate diagnoses of tuberculosis from chest radiographs.Frontiers in medicine · 2024Article
- Machine learning based tuberculosis (ML-TB) health predictor model: early TB health disease prediction with ML models for prevention in developing countries.PeerJ. Computer science · 2024Article
- Advances in the field of developing biomarkers for re-irradiation: a how-to guide to small, powerful data sets and artificial intelligence.Expert review of precision medicine and drug development · 2024Article
- A Wearable Assistant Device for the Hearing Impaired to Recognize Emergency Vehicle Sirens with Edge Computing.Sensors (Basel, Switzerland) · 2023Article
- Deep learning approach for early prediction of COVID-19 mortality using chest X-ray and electronic health records.BMC bioinformatics · 2023Article
- Article
- BézierSeg: Parametric Shape Representation for Fast Object Segmentation in Medical Images.Life (Basel, Switzerland) · 2023Article
- Early detection of tuberculosis using hybrid feature descriptors and deep learning network.Polish journal of radiology · 2023Article
- Drug-Resistant Tuberculosis Treatment Recommendation, and Multi-Class Tuberculosis Detection and Classification Using Ensemble Deep Learning-Based System.Pharmaceuticals (Basel, Switzerland) · 2022Article
- Hierarchical Voting-Based Feature Selection and Ensemble Learning Model Scheme for Glioma Grading with Clinical and Molecular Characteristics.International journal of molecular sciences · 2022Article
- Article
- Deep Learning for the Differential Diagnosis between Transient Osteoporosis and Avascular Necrosis of the Hip.Diagnostics (Basel, Switzerland) · 2022Article
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3 authors at 1 institution in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
Tuberculosis (TB) is known as a potentially dangerous and infectious disease that affects mostly lungs worldwide. The detection and treatment of TB at an early stage are critical for preventing the disease and decreasing the risk of mortality and transmission of it to others. Nowadays, as the most common medical imaging technique, chest radiography (CXR) is useful for determining thoracic diseases. Computer-aided detection (CADe) systems are also crucial mechanisms to provide more reliable, efficient, and systematic approaches with accelerating the decision-making process of clinicians. In this study, we propose voting and preprocessing variations-based ensemble CNN model for TB detection. We utilize 40 different variations in fine-tuned CNN models based on InceptionV3 and Xception by also using CLAHE (contrast-limited adaptive histogram equalization) preprocessing technique and 10 different image transformations for data augmentation types. After analyzing all these combination schemes, three or five best classifier models are selected as base learners for voting operations. We apply the Bayesian optimization-based weighted voting and the average of probabilities as a combination rule in soft voting methods on two TB CXR image datasets to get better results in various numbers of models. The computational results indicate that the proposed method achieves 97.500% and 97.699% accuracy rates on Montgomery and Shenzhen datasets, respectively. Furthermore, our method outperforms state-of-the-art results for the two TB detection datasets in terms of accuracy rate.
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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.