An explainable AI-based interpreter for deep-dual-wave based Unet framework with a cross-attention layer-based dental caries segmentation and classification.
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| Title: | An explainable AI-based interpreter for deep-dual-wave based Unet framework with a cross-attention layer-based dental caries segmentation and classification. |
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| Authors: | Aelgani, Vivekanand, Singh, Akansha, Narayana, V. A. |
| Source: | Network Modeling & Analysis in Health Informatics & Bioinformatics; 3/7/2026, Vol. 15 Issue 1, p1-21, 21p |
| Subject Terms: | DEFECT tracking (Computer software development) |
| Abstract: | Software defect prediction plays a critical role in improving software quality by enabling early identification of fault-prone modules. However, existing prediction approaches often suffer from limited generalization under class imbalance, lack of interpretability, and insufficient practical usability for real-world development environments. To address these challenges, this study proposes a graphical user interface (GUI)–integrated explainable multi-branch multi-fusion GoogleNet framework, referred to as Xai-MBMFG, for software defect prediction. The proposed framework combines multiple complementary learning branches, including dilated convolutions, self-attention mechanisms, and enhanced convolutional layers, to capture diverse defect-related patterns. In addition, statistical feature measures such as entropy, cosine coefficient, and Bhattacharyya coefficient are employed to reduce uncertainty and improve feature discrimination prior to deep learning. Model transparency is enhanced through the integration of explainable AI techniques, specifically LIME and SHAP, which provide localized explanations for individual predictions. The effectiveness of the proposed approach is evaluated on three widely used benchmark datasets—AEEM, NASA, and PROMISE—using multiple performance metrics. The implementation is done by the PYTHON. The experimental results demonstrate that the segmentation model achieves the highest precision (0.9857). Ablation and cross-validation studies further confirm the contribution and stability of each architectural component. [ABSTRACT FROM AUTHOR] |
| Copyright of Network Modeling & Analysis in Health Informatics & Bioinformatics is the property of Springer Nature 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: | Complementary Index |
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| Items | – Name: Title Label: Title Group: Ti Data: An explainable AI-based interpreter for deep-dual-wave based Unet framework with a cross-attention layer-based dental caries segmentation and classification. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Aelgani%2C+Vivekanand%22">Aelgani, Vivekanand</searchLink><br /><searchLink fieldCode="AR" term="%22Singh%2C+Akansha%22">Singh, Akansha</searchLink><br /><searchLink fieldCode="AR" term="%22Narayana%2C+V%2E+A%2E%22">Narayana, V. A.</searchLink> – Name: TitleSource Label: Source Group: Src Data: Network Modeling & Analysis in Health Informatics & Bioinformatics; 3/7/2026, Vol. 15 Issue 1, p1-21, 21p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22DEFECT+tracking+%28Computer+software+development%29%22">DEFECT tracking (Computer software development)</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Software defect prediction plays a critical role in improving software quality by enabling early identification of fault-prone modules. However, existing prediction approaches often suffer from limited generalization under class imbalance, lack of interpretability, and insufficient practical usability for real-world development environments. To address these challenges, this study proposes a graphical user interface (GUI)–integrated explainable multi-branch multi-fusion GoogleNet framework, referred to as Xai-MBMFG, for software defect prediction. The proposed framework combines multiple complementary learning branches, including dilated convolutions, self-attention mechanisms, and enhanced convolutional layers, to capture diverse defect-related patterns. In addition, statistical feature measures such as entropy, cosine coefficient, and Bhattacharyya coefficient are employed to reduce uncertainty and improve feature discrimination prior to deep learning. Model transparency is enhanced through the integration of explainable AI techniques, specifically LIME and SHAP, which provide localized explanations for individual predictions. The effectiveness of the proposed approach is evaluated on three widely used benchmark datasets—AEEM, NASA, and PROMISE—using multiple performance metrics. The implementation is done by the PYTHON. The experimental results demonstrate that the segmentation model achieves the highest precision (0.9857). Ablation and cross-validation studies further confirm the contribution and stability of each architectural component. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Network Modeling & Analysis in Health Informatics & Bioinformatics is the property of Springer Nature 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: 10.1007/s13721-026-00752-0 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 21 StartPage: 1 Subjects: – SubjectFull: DEFECT tracking (Computer software development) Type: general Titles: – TitleFull: An explainable AI-based interpreter for deep-dual-wave based Unet framework with a cross-attention layer-based dental caries segmentation and classification. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Aelgani, Vivekanand – PersonEntity: Name: NameFull: Singh, Akansha – PersonEntity: Name: NameFull: Narayana, V. A. IsPartOfRelationships: – BibEntity: Dates: – D: 07 M: 03 Text: 3/7/2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 21926662 Numbering: – Type: volume Value: 15 – Type: issue Value: 1 Titles: – TitleFull: Network Modeling & Analysis in Health Informatics & Bioinformatics Type: main |
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