Numerical Interpretation of Variation Indices of Tumor Outline Tracking Using Artificial Intelligence Mapping.
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| Title: | Numerical Interpretation of Variation Indices of Tumor Outline Tracking Using Artificial Intelligence Mapping. |
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| Authors: | Mortazavy Beni, Hamidreza1 (AUTHOR) HRM.Beni@iau.ac.ir |
| Source: | Biomedical Engineering & Computational Biology. 4/15/2026, Vol. 17, p1-16. 16p. |
| Subjects: | Cancer diagnosis, Mammograms, Feature extraction, Artificial intelligence, Deep learning, Diagnosis, Machine learning |
| Abstract: | Background: Accurate differentiation between benign and malignant breast tumors is critical for early diagnosis and treatment planning. Traditional approaches often rely on whole-image processing; however, the tumor contour contains rich morphological cues that can independently support malignancy assessment. Leveraging these contour-based features using artificial intelligence (AI) can enhance diagnostic specificity and interpretability. Objective: This study aims to evaluate the diagnostic potential of tumor outline features extracted from mammographic images using deep learning models, with a focus on interpreting their variations numerically and biologically. It also investigates whether combining deep features (ensemble approach) can improve classification accuracy. Methods: A public dataset of 100 mammography tumor contours was analyzed. Eight deep learning models (ResNet50, Xception65, VGG16, AlexNet, DenseNet, GoogLeNet, Inception-v3, and a feature-level ensemble) were used for feature extraction. These features were then classified using 5 machine learning algorithms: SVM, KNN, DT, Naive Bayes, and a shallow neural network. Performance metrics included accuracy, sensitivity, specificity, and precision. Results: Xception65 with Naive Bayes achieved 97.97% accuracy, while the feature ensemble with an ensemble classifier achieved 96.96% accuracy, 95.45% sensitivity, and 98.48% specificity. Naive Bayes consistently outperformed other classifiers in integrating deep contour features. Conclusion and Clinical Interpretation: Tumor contour-based analysis provides biologically meaningful indicators of malignancy—such as irregularity, spiculation, and shape complexity—without relying on full pixel intensity. The results demonstrate that outline-driven AI analysis can enhance breast cancer screening by offering a low-complexity, high-performance diagnostic tool. Future integration into clinical workflows may aid radiologists in real-time and reduce false positives in mammographic diagnosis. [ABSTRACT FROM AUTHOR] |
| Copyright of Biomedical Engineering & Computational Biology is the property of Sage Publications Inc. 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: 193059413 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Numerical Interpretation of Variation Indices of Tumor Outline Tracking Using Artificial Intelligence Mapping. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Mortazavy+Beni%2C+Hamidreza%22">Mortazavy Beni, Hamidreza</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> HRM.Beni@iau.ac.ir</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Biomedical+Engineering+%26+Computational+Biology%22">Biomedical Engineering & Computational Biology</searchLink>. 4/15/2026, Vol. 17, p1-16. 16p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Cancer+diagnosis%22">Cancer diagnosis</searchLink><br /><searchLink fieldCode="DE" term="%22Mammograms%22">Mammograms</searchLink><br /><searchLink fieldCode="DE" term="%22Feature+extraction%22">Feature extraction</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Diagnosis%22">Diagnosis</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Background: Accurate differentiation between benign and malignant breast tumors is critical for early diagnosis and treatment planning. Traditional approaches often rely on whole-image processing; however, the tumor contour contains rich morphological cues that can independently support malignancy assessment. Leveraging these contour-based features using artificial intelligence (AI) can enhance diagnostic specificity and interpretability. Objective: This study aims to evaluate the diagnostic potential of tumor outline features extracted from mammographic images using deep learning models, with a focus on interpreting their variations numerically and biologically. It also investigates whether combining deep features (ensemble approach) can improve classification accuracy. Methods: A public dataset of 100 mammography tumor contours was analyzed. Eight deep learning models (ResNet50, Xception65, VGG16, AlexNet, DenseNet, GoogLeNet, Inception-v3, and a feature-level ensemble) were used for feature extraction. These features were then classified using 5 machine learning algorithms: SVM, KNN, DT, Naive Bayes, and a shallow neural network. Performance metrics included accuracy, sensitivity, specificity, and precision. Results: Xception65 with Naive Bayes achieved 97.97% accuracy, while the feature ensemble with an ensemble classifier achieved 96.96% accuracy, 95.45% sensitivity, and 98.48% specificity. Naive Bayes consistently outperformed other classifiers in integrating deep contour features. Conclusion and Clinical Interpretation: Tumor contour-based analysis provides biologically meaningful indicators of malignancy—such as irregularity, spiculation, and shape complexity—without relying on full pixel intensity. The results demonstrate that outline-driven AI analysis can enhance breast cancer screening by offering a low-complexity, high-performance diagnostic tool. Future integration into clinical workflows may aid radiologists in real-time and reduce false positives in mammographic diagnosis. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Biomedical Engineering & Computational Biology is the property of Sage Publications Inc. 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: 10.1177/11795972261441395 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 16 StartPage: 1 Subjects: – SubjectFull: Cancer diagnosis Type: general – SubjectFull: Mammograms Type: general – SubjectFull: Feature extraction Type: general – SubjectFull: Artificial intelligence Type: general – SubjectFull: Deep learning Type: general – SubjectFull: Diagnosis Type: general – SubjectFull: Machine learning Type: general Titles: – TitleFull: Numerical Interpretation of Variation Indices of Tumor Outline Tracking Using Artificial Intelligence Mapping. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Mortazavy Beni, Hamidreza IsPartOfRelationships: – BibEntity: Dates: – D: 15 M: 04 Text: 4/15/2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 11795972 Numbering: – Type: volume Value: 17 Titles: – TitleFull: Biomedical Engineering & Computational Biology Type: main |
| ResultId | 1 |
