ArticleComputers in biology and medicine2022
Automatic detection of pneumonia in chest X-ray images using textural features.
Article in Computers in biology and medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Artificial intelligence for chest radiography: an overview of techniques, challenges, and future directions.npj health systems · 2026Review
- Ensemble localized patch residual convolution neural networks for pneumonia detection using chest X-ray.BMC medical informatics and decision making · 2026Article
- Article
- A Comparative Evaluation of Meta-Learning Models for Few-Shot Chest X-Ray Disease Classification.Diagnostics (Basel, Switzerland) · 2025Article
- PneumoNet: Deep Neural Network for Advanced Pneumonia Detection.Current medical imaging · 2025Article
- FLPneXAINet: Federated deep learning and explainable AI for improved pneumonia prediction utilizing GAN-augmented chest X-ray data.PloS one · 2025Article
- Enhancing deep learning pre-trained networks on diabetic retinopathy fundus photographs with SLIC-G.Medical & biological engineering & computing · 2024Article
- Deep Learning for Pneumonia Detection in Chest X-ray Images: A Comprehensive Survey.Journal of imaging · 2024Review
- Clinical applications of artificial intelligence in identification and management of bacterial infection: Systematic review and meta-analysis.Saudi journal of biological sciences · 2024Review
- DLAAD-deep learning algorithms assisted diagnosis of chest disease using radiographic medical images.Frontiers in medicine · 2024Article
- Deep Transfer Learning with Enhanced Feature Fusion for Detection of Abnormalities in X-ray Images.Cancers · 2023Article
- Machine Learning and Deep Learning in Cardiothoracic Imaging: A Scoping Review.Diagnostics (Basel, Switzerland) · 2022Article
- Screening Lung Diseases Using Cascaded Feature Generation and Selection Strategies.Healthcare (Basel, Switzerland) · 2022Article
- Disease Localization and Severity Assessment in Chest X-Ray Images using Multi-Stage Superpixels Classification.Computer methods and programs in biomedicine · 2022Article
- The Use of Chest Radiographs and Machine Learning Model for the Rapid Detection of Pneumonitis in Pediatric.BioMed research international · 2022Article
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4 authors.
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Abstract
Fast and accurate diagnosis is critical for the triage and management of pneumonia, particularly in the current scenario of a COVID-19 pandemic, where this pathology is a major symptom of the infection. With the objective of providing tools for that purpose, this study assesses the potential of three textural image characterisation methods: radiomics, fractal dimension and the recently developed superpixel-based histon, as biomarkers to be used for training Artificial Intelligence (AI) models in order to detect pneumonia in chest X-ray images. Models generated from three different AI algorithms have been studied: K-Nearest Neighbors, Support Vector Machine and Random Forest. Two open-access image datasets were used in this study. In the first one, a dataset composed of paediatric chest X-ray, the best performing generated models achieved an 83.3% accuracy with 89% sensitivity for radiomics, 89.9% accuracy with 93.6% sensitivity for fractal dimension and 91.3% accuracy with 90.5% sensitivity for superpixels based histon. Second, a dataset derived from an image repository developed primarily as a tool for studying COVID-19 was used. For this dataset, the best performing generated models resulted in a 95.3% accuracy with 99.2% sensitivity for radiomics, 99% accuracy with 100% sensitivity for fractal dimension and 99% accuracy with 98.6% sensitivity for superpixel-based histons. The results confirm the validity of the tested methods as reliable and easy-to-implement automatic diagnostic tools for pneumonia.
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