PneuX-Net: An Enhanced Feature Extraction and Transformation Approach for Pneumonia Detection in X-Ray Images
<p dir="ltr">Pneumonia is a critical respiratory condition characterized by inflammation in the alveoli, leading to impaired gas exchange and severe respiratory distress. It poses a significant threat to high-risk populations, including neonates, geriatric patients, and immunocomprom...
محفوظ في:
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| مؤلفون آخرون: | , , , |
| منشور في: |
2025
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| _version_ | 1864513533735075840 |
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| author | Kashif Munir (6182237) |
| author2 | Muhammad Usama Tanveer (22225360) Hasan J. Alyamani (22502435) Amine Bermak (1895947) Atiq Ur Rehman (22502438) |
| author2_role | author author author author |
| author_facet | Kashif Munir (6182237) Muhammad Usama Tanveer (22225360) Hasan J. Alyamani (22502435) Amine Bermak (1895947) Atiq Ur Rehman (22502438) |
| author_role | author |
| dc.creator.none.fl_str_mv | Kashif Munir (6182237) Muhammad Usama Tanveer (22225360) Hasan J. Alyamani (22502435) Amine Bermak (1895947) Atiq Ur Rehman (22502438) |
| dc.date.none.fl_str_mv | 2025-05-19T12:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1109/access.2025.3568885 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/PneuX-Net_An_Enhanced_Feature_Extraction_and_Transformation_Approach_for_Pneumonia_Detection_in_X-Ray_Images/30454700 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Health sciences Health services and systems Information and computing sciences Artificial intelligence PneuX-Net pneumonia detection machine learning medical diagnosis healthcare AI Pneumonia Accuracy Feature extraction X-ray imaging Deep learning Solid modeling Computational modeling Machine learning Lungs Bayes methods |
| dc.title.none.fl_str_mv | PneuX-Net: An Enhanced Feature Extraction and Transformation Approach for Pneumonia Detection in X-Ray Images |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">Pneumonia is a critical respiratory condition characterized by inflammation in the alveoli, leading to impaired gas exchange and severe respiratory distress. It poses a significant threat to high-risk populations, including neonates, geriatric patients, and immunocompromised individuals. Early and precise detection is crucial to optimizing treatment strategies and reducing morbidity and mortality rates. To address this problem, we propose PneuX-Net, an ensemble-based feature extraction framework that integrates multiple machine learning (ML) models Random Forest (RF), Gaussian Naïve Bayes (GNB) and K-Nearest Centroid (KNC). Random Forest (RF) builds multiple decision trees to identify complex, non-linear patterns within the feature space. Gaussian Naïve Bayes (GNB) utilizes a probabilistic framework based on Bayes’ theorem to manage uncertainty in feature distributions. K-Nearest Centroid (KNC) improves class distinction by clustering feature vectors according to their proximity to centroids. The ensemble methodology harnesses the complementary strengths of these models, improving feature representation and mitigating overfitting, a prevalent issue in white-box models. To validate the effectiveness of PneuX-Net, we conduct extensive experimentation using a 10-fold cross-validation approach. Our results demonstrate that the PneuX-Net+KNC model achieves a remarkable 99.91% accuracy, highlighting its robustness and reliability in pneumonia classification tasks. This ensemble-driven methodology underscores the potential of machine learning in augmenting clinical decision-making by providing an accurate and automated pneumonia detection framework.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/deed.en" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2025.3568885" target="_blank">https://dx.doi.org/10.1109/access.2025.3568885</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_851f14dcf72b83952ca5f849dbfc37f5 |
| identifier_str_mv | 10.1109/access.2025.3568885 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/30454700 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | PneuX-Net: An Enhanced Feature Extraction and Transformation Approach for Pneumonia Detection in X-Ray ImagesKashif Munir (6182237)Muhammad Usama Tanveer (22225360)Hasan J. Alyamani (22502435)Amine Bermak (1895947)Atiq Ur Rehman (22502438)Health sciencesHealth services and systemsInformation and computing sciencesArtificial intelligencePneuX-Netpneumonia detectionmachine learningmedical diagnosishealthcare AIPneumoniaAccuracyFeature extractionX-ray imagingDeep learningSolid modelingComputational modelingMachine learningLungsBayes methods<p dir="ltr">Pneumonia is a critical respiratory condition characterized by inflammation in the alveoli, leading to impaired gas exchange and severe respiratory distress. It poses a significant threat to high-risk populations, including neonates, geriatric patients, and immunocompromised individuals. Early and precise detection is crucial to optimizing treatment strategies and reducing morbidity and mortality rates. To address this problem, we propose PneuX-Net, an ensemble-based feature extraction framework that integrates multiple machine learning (ML) models Random Forest (RF), Gaussian Naïve Bayes (GNB) and K-Nearest Centroid (KNC). Random Forest (RF) builds multiple decision trees to identify complex, non-linear patterns within the feature space. Gaussian Naïve Bayes (GNB) utilizes a probabilistic framework based on Bayes’ theorem to manage uncertainty in feature distributions. K-Nearest Centroid (KNC) improves class distinction by clustering feature vectors according to their proximity to centroids. The ensemble methodology harnesses the complementary strengths of these models, improving feature representation and mitigating overfitting, a prevalent issue in white-box models. To validate the effectiveness of PneuX-Net, we conduct extensive experimentation using a 10-fold cross-validation approach. Our results demonstrate that the PneuX-Net+KNC model achieves a remarkable 99.91% accuracy, highlighting its robustness and reliability in pneumonia classification tasks. This ensemble-driven methodology underscores the potential of machine learning in augmenting clinical decision-making by providing an accurate and automated pneumonia detection framework.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/deed.en" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2025.3568885" target="_blank">https://dx.doi.org/10.1109/access.2025.3568885</a></p>2025-05-19T12:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1109/access.2025.3568885https://figshare.com/articles/journal_contribution/PneuX-Net_An_Enhanced_Feature_Extraction_and_Transformation_Approach_for_Pneumonia_Detection_in_X-Ray_Images/30454700CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/304547002025-05-19T12:00:00Z |
| spellingShingle | PneuX-Net: An Enhanced Feature Extraction and Transformation Approach for Pneumonia Detection in X-Ray Images Kashif Munir (6182237) Health sciences Health services and systems Information and computing sciences Artificial intelligence PneuX-Net pneumonia detection machine learning medical diagnosis healthcare AI Pneumonia Accuracy Feature extraction X-ray imaging Deep learning Solid modeling Computational modeling Machine learning Lungs Bayes methods |
| status_str | publishedVersion |
| title | PneuX-Net: An Enhanced Feature Extraction and Transformation Approach for Pneumonia Detection in X-Ray Images |
| title_full | PneuX-Net: An Enhanced Feature Extraction and Transformation Approach for Pneumonia Detection in X-Ray Images |
| title_fullStr | PneuX-Net: An Enhanced Feature Extraction and Transformation Approach for Pneumonia Detection in X-Ray Images |
| title_full_unstemmed | PneuX-Net: An Enhanced Feature Extraction and Transformation Approach for Pneumonia Detection in X-Ray Images |
| title_short | PneuX-Net: An Enhanced Feature Extraction and Transformation Approach for Pneumonia Detection in X-Ray Images |
| title_sort | PneuX-Net: An Enhanced Feature Extraction and Transformation Approach for Pneumonia Detection in X-Ray Images |
| topic | Health sciences Health services and systems Information and computing sciences Artificial intelligence PneuX-Net pneumonia detection machine learning medical diagnosis healthcare AI Pneumonia Accuracy Feature extraction X-ray imaging Deep learning Solid modeling Computational modeling Machine learning Lungs Bayes methods |