ArticleJournal of grid computing2022
Secondary Pulmonary Tuberculosis Identification Via pseudo-Zernike Moment and Deep Stacked Sparse Autoencoder.
Article in Journal of grid computing, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Ensemble deep learning architectures for detecting pulmonary tuberculosis in chest X-rays.Scientific reports · 2026Article
- A multistage framework for respiratory disease detection and assessing severity in chest X-ray images.Scientific reports · 2024Article
- Automatic Intelligent System Using Medical of Things for Multiple Sclerosis Detection.Computational intelligence and neuroscience · 2023Article
- Two-dimensional reciprocal cross entropy multi-threshold combined with improved firefly algorithm for lung parenchyma segmentation of COVID-19 CT image.Biomedical signal processing and control · 2022Article
- Disease Localization and Severity Assessment in Chest X-Ray Images using Multi-Stage Superpixels Classification.Computer methods and programs in biomedicine · 2022Article
- Optimal Deep Stacked Sparse Autoencoder Based Osteosarcoma Detection and Classification Model.Healthcare (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
5 authors.
Funding
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Abstract
Secondary pulmonary tuberculosis (SPT) is one of the top ten causes of death from a single infectious agent. To recognize SPT more accurately, this paper proposes a novel artificial intelligence model, which uses Pseudo Zernike moment (PZM) as the feature extractor and deep stacked sparse autoencoder (DSSAE) as the classifier. In addition, 18-way data augmentation is employed to avoid overfitting. This model is abbreviated as PZM-DSSAE. The ten runs of 10-fold cross-validation show this model achieves a sensitivity of 93.33% ± 1.47%, a specificity of 93.13% ± 0.95%, a precision of 93.15% ± 0.89%, an accuracy of 93.23% ± 0.81%, and an F1 score of 93.23% ± 0.83%. The area-under-curve reaches 0.9739. This PZM-DSSAE is superior to 5 state-of-the-art approaches.
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Registered trials
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