State-of-the-art methods for X-ray image classification. Summarised in terms of the classifier, preprocessing, and features extraction used and their performance using the different datasets. CLAHE: Contrast limited adaptive histogram equalization. DT: Decision Tree, HOG: Histogram of Oriented Gradients, WMF: Weighted Median Filtering, LSTM: Long short-term memory. PWLGBP: Weighted Local Gabor Binary Pattern. ENNSA: Ensemble Neural Net Sentinel Algorithm. IGLCM: Insistent Grey Level Co-occurrence Matrix. DF-GAN: Deep Fusion Generative Adversarial Networks. The performance metrics are the True Positive Rate, recall or Sensitivity (TPR), the True Negative Rate, Negative Recall, or Specificity (TNR), and the Accuracy Rate (ACC).
<p>State-of-the-art methods for X-ray image classification. Summarised in terms of the classifier, preprocessing, and features extraction used and their performance using the different datasets. CLAHE: Contrast limited adaptive histogram equalization. DT: Decision Tree, HOG: Histogram of Orien...
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| منشور في: |
2025
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| _version_ | 1852021334775693313 |
|---|---|
| author | Antonio Quintero-Rincón (21087716) |
| author2 | Ricardo Di-Pasquale (21087719) Karina Quintero-Rodríguez (21087722) Hadj Batatia (9606336) |
| author2_role | author author author |
| author_facet | Antonio Quintero-Rincón (21087716) Ricardo Di-Pasquale (21087719) Karina Quintero-Rodríguez (21087722) Hadj Batatia (9606336) |
| author_role | author |
| dc.creator.none.fl_str_mv | Antonio Quintero-Rincón (21087716) Ricardo Di-Pasquale (21087719) Karina Quintero-Rodríguez (21087722) Hadj Batatia (9606336) |
| dc.date.none.fl_str_mv | 2025-04-14T20:04:05Z |
| dc.identifier.none.fl_str_mv | 10.1371/journal.pone.0320706.t005 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/dataset/State-of-the-art_methods_for_X-ray_image_classification_Summarised_in_terms_of_the_classifier_preprocessing_and_features_extraction_used_and_their_performance_using_the_different_datasets_CLAHE_Contrast_limited_adaptive_histogram_equalizati/28791185 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Medicine Biotechnology Science Policy Space Science Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified proposed method implements open research field minimum covariance determinant image texture analysis developing automated tools conditional indices extracted conditional indices ), classic performance metrics true positive rate ray public dataset ray images based false negative rate false discovery rate positive predictive values image texture features experimental results demonstrating ray chest images work proposes using detect abnormal x singular value decomposition single parameter acts imbalanced chest x singular values chest x ray attenuation two features results show decomposition proportions tested using estimated using accuracy rate classify x without applying viral pneumonia total cost tissues affected tissue attenuation reducing misclassification parametric distribution paper presents paper introduces lung opacity collinearity diagnosis bandwidth yielded art methods &# 8220 |
| dc.title.none.fl_str_mv | State-of-the-art methods for X-ray image classification. Summarised in terms of the classifier, preprocessing, and features extraction used and their performance using the different datasets. CLAHE: Contrast limited adaptive histogram equalization. DT: Decision Tree, HOG: Histogram of Oriented Gradients, WMF: Weighted Median Filtering, LSTM: Long short-term memory. PWLGBP: Weighted Local Gabor Binary Pattern. ENNSA: Ensemble Neural Net Sentinel Algorithm. IGLCM: Insistent Grey Level Co-occurrence Matrix. DF-GAN: Deep Fusion Generative Adversarial Networks. The performance metrics are the True Positive Rate, recall or Sensitivity (TPR), the True Negative Rate, Negative Recall, or Specificity (TNR), and the Accuracy Rate (ACC). |
| dc.type.none.fl_str_mv | Dataset info:eu-repo/semantics/publishedVersion dataset |
| description | <p>State-of-the-art methods for X-ray image classification. Summarised in terms of the classifier, preprocessing, and features extraction used and their performance using the different datasets. CLAHE: Contrast limited adaptive histogram equalization. DT: Decision Tree, HOG: Histogram of Oriented Gradients, WMF: Weighted Median Filtering, LSTM: Long short-term memory. PWLGBP: Weighted Local Gabor Binary Pattern. ENNSA: Ensemble Neural Net Sentinel Algorithm. IGLCM: Insistent Grey Level Co-occurrence Matrix. DF-GAN: Deep Fusion Generative Adversarial Networks. The performance metrics are the True Positive Rate, recall or Sensitivity (TPR), the True Negative Rate, Negative Recall, or Specificity (TNR), and the Accuracy Rate (ACC).</p> |
| eu_rights_str_mv | openAccess |
| id | Manara_5664ce91f5b35d3b63dc8d654dcd2efc |
| identifier_str_mv | 10.1371/journal.pone.0320706.t005 |
| network_acronym_str | Manara |
| network_name_str | ManaraRepo |
| oai_identifier_str | oai:figshare.com:article/28791185 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | State-of-the-art methods for X-ray image classification. Summarised in terms of the classifier, preprocessing, and features extraction used and their performance using the different datasets. CLAHE: Contrast limited adaptive histogram equalization. DT: Decision Tree, HOG: Histogram of Oriented Gradients, WMF: Weighted Median Filtering, LSTM: Long short-term memory. PWLGBP: Weighted Local Gabor Binary Pattern. ENNSA: Ensemble Neural Net Sentinel Algorithm. IGLCM: Insistent Grey Level Co-occurrence Matrix. DF-GAN: Deep Fusion Generative Adversarial Networks. The performance metrics are the True Positive Rate, recall or Sensitivity (TPR), the True Negative Rate, Negative Recall, or Specificity (TNR), and the Accuracy Rate (ACC).Antonio Quintero-Rincón (21087716)Ricardo Di-Pasquale (21087719)Karina Quintero-Rodríguez (21087722)Hadj Batatia (9606336)MedicineBiotechnologyScience PolicySpace ScienceEnvironmental Sciences not elsewhere classifiedBiological Sciences not elsewhere classifiedMathematical Sciences not elsewhere classifiedInformation Systems not elsewhere classifiedproposed method implementsopen research fieldminimum covariance determinantimage texture analysisdeveloping automated toolsconditional indices extractedconditional indices ),classic performance metricstrue positive rateray public datasetray images basedfalse negative ratefalse discovery ratepositive predictive valuesimage texture featuresexperimental results demonstratingray chest imageswork proposes usingdetect abnormal xsingular value decompositionsingle parameter actsimbalanced chest xsingular valueschest xray attenuationtwo featuresresults showdecomposition proportionstested usingestimated usingaccuracy rateclassify xwithout applyingviral pneumoniatotal costtissues affectedtissue attenuationreducing misclassificationparametric distributionpaper presentspaper introduceslung opacitycollinearity diagnosisbandwidth yieldedart methods&# 8220<p>State-of-the-art methods for X-ray image classification. Summarised in terms of the classifier, preprocessing, and features extraction used and their performance using the different datasets. CLAHE: Contrast limited adaptive histogram equalization. DT: Decision Tree, HOG: Histogram of Oriented Gradients, WMF: Weighted Median Filtering, LSTM: Long short-term memory. PWLGBP: Weighted Local Gabor Binary Pattern. ENNSA: Ensemble Neural Net Sentinel Algorithm. IGLCM: Insistent Grey Level Co-occurrence Matrix. DF-GAN: Deep Fusion Generative Adversarial Networks. The performance metrics are the True Positive Rate, recall or Sensitivity (TPR), the True Negative Rate, Negative Recall, or Specificity (TNR), and the Accuracy Rate (ACC).</p>2025-04-14T20:04:05ZDatasetinfo:eu-repo/semantics/publishedVersiondataset10.1371/journal.pone.0320706.t005https://figshare.com/articles/dataset/State-of-the-art_methods_for_X-ray_image_classification_Summarised_in_terms_of_the_classifier_preprocessing_and_features_extraction_used_and_their_performance_using_the_different_datasets_CLAHE_Contrast_limited_adaptive_histogram_equalizati/28791185CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/287911852025-04-14T20:04:05Z |
| spellingShingle | State-of-the-art methods for X-ray image classification. Summarised in terms of the classifier, preprocessing, and features extraction used and their performance using the different datasets. CLAHE: Contrast limited adaptive histogram equalization. DT: Decision Tree, HOG: Histogram of Oriented Gradients, WMF: Weighted Median Filtering, LSTM: Long short-term memory. PWLGBP: Weighted Local Gabor Binary Pattern. ENNSA: Ensemble Neural Net Sentinel Algorithm. IGLCM: Insistent Grey Level Co-occurrence Matrix. DF-GAN: Deep Fusion Generative Adversarial Networks. The performance metrics are the True Positive Rate, recall or Sensitivity (TPR), the True Negative Rate, Negative Recall, or Specificity (TNR), and the Accuracy Rate (ACC). Antonio Quintero-Rincón (21087716) Medicine Biotechnology Science Policy Space Science Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified proposed method implements open research field minimum covariance determinant image texture analysis developing automated tools conditional indices extracted conditional indices ), classic performance metrics true positive rate ray public dataset ray images based false negative rate false discovery rate positive predictive values image texture features experimental results demonstrating ray chest images work proposes using detect abnormal x singular value decomposition single parameter acts imbalanced chest x singular values chest x ray attenuation two features results show decomposition proportions tested using estimated using accuracy rate classify x without applying viral pneumonia total cost tissues affected tissue attenuation reducing misclassification parametric distribution paper presents paper introduces lung opacity collinearity diagnosis bandwidth yielded art methods &# 8220 |
| status_str | publishedVersion |
| title | State-of-the-art methods for X-ray image classification. Summarised in terms of the classifier, preprocessing, and features extraction used and their performance using the different datasets. CLAHE: Contrast limited adaptive histogram equalization. DT: Decision Tree, HOG: Histogram of Oriented Gradients, WMF: Weighted Median Filtering, LSTM: Long short-term memory. PWLGBP: Weighted Local Gabor Binary Pattern. ENNSA: Ensemble Neural Net Sentinel Algorithm. IGLCM: Insistent Grey Level Co-occurrence Matrix. DF-GAN: Deep Fusion Generative Adversarial Networks. The performance metrics are the True Positive Rate, recall or Sensitivity (TPR), the True Negative Rate, Negative Recall, or Specificity (TNR), and the Accuracy Rate (ACC). |
| title_full | State-of-the-art methods for X-ray image classification. Summarised in terms of the classifier, preprocessing, and features extraction used and their performance using the different datasets. CLAHE: Contrast limited adaptive histogram equalization. DT: Decision Tree, HOG: Histogram of Oriented Gradients, WMF: Weighted Median Filtering, LSTM: Long short-term memory. PWLGBP: Weighted Local Gabor Binary Pattern. ENNSA: Ensemble Neural Net Sentinel Algorithm. IGLCM: Insistent Grey Level Co-occurrence Matrix. DF-GAN: Deep Fusion Generative Adversarial Networks. The performance metrics are the True Positive Rate, recall or Sensitivity (TPR), the True Negative Rate, Negative Recall, or Specificity (TNR), and the Accuracy Rate (ACC). |
| title_fullStr | State-of-the-art methods for X-ray image classification. Summarised in terms of the classifier, preprocessing, and features extraction used and their performance using the different datasets. CLAHE: Contrast limited adaptive histogram equalization. DT: Decision Tree, HOG: Histogram of Oriented Gradients, WMF: Weighted Median Filtering, LSTM: Long short-term memory. PWLGBP: Weighted Local Gabor Binary Pattern. ENNSA: Ensemble Neural Net Sentinel Algorithm. IGLCM: Insistent Grey Level Co-occurrence Matrix. DF-GAN: Deep Fusion Generative Adversarial Networks. The performance metrics are the True Positive Rate, recall or Sensitivity (TPR), the True Negative Rate, Negative Recall, or Specificity (TNR), and the Accuracy Rate (ACC). |
| title_full_unstemmed | State-of-the-art methods for X-ray image classification. Summarised in terms of the classifier, preprocessing, and features extraction used and their performance using the different datasets. CLAHE: Contrast limited adaptive histogram equalization. DT: Decision Tree, HOG: Histogram of Oriented Gradients, WMF: Weighted Median Filtering, LSTM: Long short-term memory. PWLGBP: Weighted Local Gabor Binary Pattern. ENNSA: Ensemble Neural Net Sentinel Algorithm. IGLCM: Insistent Grey Level Co-occurrence Matrix. DF-GAN: Deep Fusion Generative Adversarial Networks. The performance metrics are the True Positive Rate, recall or Sensitivity (TPR), the True Negative Rate, Negative Recall, or Specificity (TNR), and the Accuracy Rate (ACC). |
| title_short | State-of-the-art methods for X-ray image classification. Summarised in terms of the classifier, preprocessing, and features extraction used and their performance using the different datasets. CLAHE: Contrast limited adaptive histogram equalization. DT: Decision Tree, HOG: Histogram of Oriented Gradients, WMF: Weighted Median Filtering, LSTM: Long short-term memory. PWLGBP: Weighted Local Gabor Binary Pattern. ENNSA: Ensemble Neural Net Sentinel Algorithm. IGLCM: Insistent Grey Level Co-occurrence Matrix. DF-GAN: Deep Fusion Generative Adversarial Networks. The performance metrics are the True Positive Rate, recall or Sensitivity (TPR), the True Negative Rate, Negative Recall, or Specificity (TNR), and the Accuracy Rate (ACC). |
| title_sort | State-of-the-art methods for X-ray image classification. Summarised in terms of the classifier, preprocessing, and features extraction used and their performance using the different datasets. CLAHE: Contrast limited adaptive histogram equalization. DT: Decision Tree, HOG: Histogram of Oriented Gradients, WMF: Weighted Median Filtering, LSTM: Long short-term memory. PWLGBP: Weighted Local Gabor Binary Pattern. ENNSA: Ensemble Neural Net Sentinel Algorithm. IGLCM: Insistent Grey Level Co-occurrence Matrix. DF-GAN: Deep Fusion Generative Adversarial Networks. The performance metrics are the True Positive Rate, recall or Sensitivity (TPR), the True Negative Rate, Negative Recall, or Specificity (TNR), and the Accuracy Rate (ACC). |
| topic | Medicine Biotechnology Science Policy Space Science Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified proposed method implements open research field minimum covariance determinant image texture analysis developing automated tools conditional indices extracted conditional indices ), classic performance metrics true positive rate ray public dataset ray images based false negative rate false discovery rate positive predictive values image texture features experimental results demonstrating ray chest images work proposes using detect abnormal x singular value decomposition single parameter acts imbalanced chest x singular values chest x ray attenuation two features results show decomposition proportions tested using estimated using accuracy rate classify x without applying viral pneumonia total cost tissues affected tissue attenuation reducing misclassification parametric distribution paper presents paper introduces lung opacity collinearity diagnosis bandwidth yielded art methods &# 8220 |