2022
DOI: 10.1016/j.compbiomed.2022.105466
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Automatic detection of pneumonia in chest X-ray images using textural features

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Cited by 43 publications
(16 citation statements)
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“…However, most of them used the Saliency Maps and Grad-CAM to localize the disease manifestations, which generate a rough disease boundary. To confront this problem, some studies have used superpixel approach to obtain the most out of CAD-assisted interpretation [ 6 , 48 , 49 ]. Ortiz-Toro et al [48] has extracted Superpixel-based histon feature, which describes the local correlations of pixel intensity levels in an image.…”
Section: Discussionmentioning
confidence: 99%
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“…However, most of them used the Saliency Maps and Grad-CAM to localize the disease manifestations, which generate a rough disease boundary. To confront this problem, some studies have used superpixel approach to obtain the most out of CAD-assisted interpretation [ 6 , 48 , 49 ]. Ortiz-Toro et al [48] has extracted Superpixel-based histon feature, which describes the local correlations of pixel intensity levels in an image.…”
Section: Discussionmentioning
confidence: 99%
“…Recently, the superpixel based disease classification and segmentation has gained significant attention [ 6 , 48 , 49 ]. Ortiz-Toro et al.…”
Section: Related Workmentioning
confidence: 99%
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“…Virus infection has been one of the most serious threats to human health throughout history. One of the most common viral infections is pneumonia [1]. Infections caused by viruses and bacteria harm the lungs [2].…”
Section: Introductionmentioning
confidence: 99%
“…Experts in [ 12 ] presented a deep-learning-based COVID-19 pneumonia diagnostic tool to distinguish COVID-19 pneumonia from negative cases by utilizing 10,182 chest X-ray radiography images. A textural image-characterization-techniques-based scheme has been proposed in [ 13 ] to analyze three ML classifiers (kNN, RF, and SVM) for the identification of COVID-19 positive cases. Their developed scheme achieved 99% accuracy on a test set and 91.3% accuracy on a training set for super-pixel-based histone image characterization.…”
Section: Introductionmentioning
confidence: 99%