Enhancing Lung Nodule Prediction Accuracy: A Data‐Driven Approach With Dynamic Feature Extraction.
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| Title: | Enhancing Lung Nodule Prediction Accuracy: A Data‐Driven Approach With Dynamic Feature Extraction. |
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| Authors: | Maqbool, Madiha1 (AUTHOR), Awais, Muhammad2 (AUTHOR), Tarif, Afshan1 (AUTHOR), Zafar, Nisar Ahmad3 (AUTHOR), Shim, Seong-O4 (AUTHOR), Hussain, Lal1,5 (AUTHOR) lal.hussain@ajku.edu.pk, Waheed, Ahsen6 (AUTHOR), Nadeem, Muhammad Amin7 (AUTHOR), He, Xiao (AUTHOR) hexiao@tsinghua.edu.cn |
| Source: | Journal of Engineering (2314-4912). 5/14/2026, Vol. 2026, p1-24. 24p. |
| Subjects: | Feature extraction, Convolutional neural networks, Lung tumors, Boosting algorithms, Image enhancement (Imaging systems), Lung cancer, Machine learning, Mathematical optimization |
| Abstract: | By combining thorough picture preprocessing with optimized feature extraction, this study offers a sophisticated approach for early lung cancer identification. It makes it possible to extract both multivariate static features and dynamic deep features from ResNet50 by improving lung image quality and segmentation to precisely designate regions of interest. By identifying intricate nonlinear and hidden patterns in lung nodules, our method improves prediction accuracy and facilitates prompt diagnosis and individualized treatment, thereby addressing the shortcomings of earlier research. We then combined features and fed to machine learning ensemble extreme boosting (XGBoost) algorithm by optimizing the hyperparameters using Bayesian optimization for improving the detection performance with and without feature selection methods. The proposed GLCM + ResNet50 method surpassed most existing methods, achieving a high accuracy of 97.62%, a Matthews Correlation Coefficient (MCC) of 94.94%, an F1‐Score of 94.03%, and an Area Under the Curve (AUC) of 0.9850 with top 400 hybrid features using Kruskal Wallis feature selection method. This hybrid approach, which effectively combines texture analysis with deep learning, demonstrates potential for robust and enhanced feature extraction. By capturing both local and global image features, this method leads to improved performance. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Engineering (2314-4912) is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 193755604 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Enhancing Lung Nodule Prediction Accuracy: A Data‐Driven Approach With Dynamic Feature Extraction. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Maqbool%2C+Madiha%22">Maqbool, Madiha</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Awais%2C+Muhammad%22">Awais, Muhammad</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Tarif%2C+Afshan%22">Tarif, Afshan</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zafar%2C+Nisar+Ahmad%22">Zafar, Nisar Ahmad</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Shim%2C+Seong-O%22">Shim, Seong-O</searchLink><relatesTo>4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Hussain%2C+Lal%22">Hussain, Lal</searchLink><relatesTo>1,5</relatesTo> (AUTHOR)<i> lal.hussain@ajku.edu.pk</i><br /><searchLink fieldCode="AR" term="%22Waheed%2C+Ahsen%22">Waheed, Ahsen</searchLink><relatesTo>6</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Nadeem%2C+Muhammad+Amin%22">Nadeem, Muhammad Amin</searchLink><relatesTo>7</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22He%2C+Xiao%22">He, Xiao</searchLink> (AUTHOR)<i> hexiao@tsinghua.edu.cn</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Journal+of+Engineering+%282314-4912%29%22">Journal of Engineering (2314-4912)</searchLink>. 5/14/2026, Vol. 2026, p1-24. 24p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Feature+extraction%22">Feature extraction</searchLink><br /><searchLink fieldCode="DE" term="%22Convolutional+neural+networks%22">Convolutional neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Lung+tumors%22">Lung tumors</searchLink><br /><searchLink fieldCode="DE" term="%22Boosting+algorithms%22">Boosting algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Image+enhancement+%28Imaging+systems%29%22">Image enhancement (Imaging systems)</searchLink><br /><searchLink fieldCode="DE" term="%22Lung+cancer%22">Lung cancer</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+optimization%22">Mathematical optimization</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: By combining thorough picture preprocessing with optimized feature extraction, this study offers a sophisticated approach for early lung cancer identification. It makes it possible to extract both multivariate static features and dynamic deep features from ResNet50 by improving lung image quality and segmentation to precisely designate regions of interest. By identifying intricate nonlinear and hidden patterns in lung nodules, our method improves prediction accuracy and facilitates prompt diagnosis and individualized treatment, thereby addressing the shortcomings of earlier research. We then combined features and fed to machine learning ensemble extreme boosting (XGBoost) algorithm by optimizing the hyperparameters using Bayesian optimization for improving the detection performance with and without feature selection methods. The proposed GLCM + ResNet50 method surpassed most existing methods, achieving a high accuracy of 97.62%, a Matthews Correlation Coefficient (MCC) of 94.94%, an F1‐Score of 94.03%, and an Area Under the Curve (AUC) of 0.9850 with top 400 hybrid features using Kruskal Wallis feature selection method. This hybrid approach, which effectively combines texture analysis with deep learning, demonstrates potential for robust and enhanced feature extraction. By capturing both local and global image features, this method leads to improved performance. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Journal of Engineering (2314-4912) is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1155/je/1367255 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 24 StartPage: 1 Subjects: – SubjectFull: Feature extraction Type: general – SubjectFull: Convolutional neural networks Type: general – SubjectFull: Lung tumors Type: general – SubjectFull: Boosting algorithms Type: general – SubjectFull: Image enhancement (Imaging systems) Type: general – SubjectFull: Lung cancer Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Mathematical optimization Type: general Titles: – TitleFull: Enhancing Lung Nodule Prediction Accuracy: A Data‐Driven Approach With Dynamic Feature Extraction. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Maqbool, Madiha – PersonEntity: Name: NameFull: Awais, Muhammad – PersonEntity: Name: NameFull: Tarif, Afshan – PersonEntity: Name: NameFull: Zafar, Nisar Ahmad – PersonEntity: Name: NameFull: Shim, Seong-O – PersonEntity: Name: NameFull: Hussain, Lal – PersonEntity: Name: NameFull: Waheed, Ahsen – PersonEntity: Name: NameFull: Nadeem, Muhammad Amin – PersonEntity: Name: NameFull: He, Xiao IsPartOfRelationships: – BibEntity: Dates: – D: 14 M: 05 Text: 5/14/2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 23144904 Numbering: – Type: volume Value: 2026 Titles: – TitleFull: Journal of Engineering (2314-4912) Type: main |
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