Scalable HMO-CNN-SVM Framework for Skin Lesion Classification: A Metaheuristic-Driven Approach With Parallelizable Optimization for Cluster Deployment.
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| Title: | Scalable HMO-CNN-SVM Framework for Skin Lesion Classification: A Metaheuristic-Driven Approach With Parallelizable Optimization for Cluster Deployment. |
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| Authors: | Fendzi Mbasso, Wulfran1 (AUTHOR) fendzi.wulfran@yahoo.fr, Harrison, Ambe2 (AUTHOR) fendzi.wulfran@yahoo.fr, Mamadiyarov, Zokir3 (AUTHOR), Liu, Zhe4 (AUTHOR), Kumar, Raman5 (AUTHOR), Shaikh, Muhammad Suhail6,7 (AUTHOR), Metwally Mahmoud, Mohamed8 (AUTHOR) |
| Source: | Biomedical Engineering & Computational Biology. 6/6/2026, Vol. 17, p1-20. 20p. |
| Subjects: | Convolutional neural networks, Metaheuristic algorithms, Computer-assisted image analysis (Medicine), Machine learning, Parallel programming, Skin disease diagnosis, Support vector machines |
| Abstract: | In medical image analysis, accurate skin lesion categorization is still a major difficulty particularly under limited data conditions and computational complexity. For automated skin cancer detection, in this work we present a scalable hybrid model combining a Convolutional Neural Network (CNN), the Harmonic Mean Optimizer (HMO), and a Support Vector Machine (SVM) classifier—termed HMO-CNN-SVM. Key CNN hyperparameters including learning rate, batch size, and kernel configuration are optimized using the HMO, so greatly boosting classification performance over manual or stationary settings. The model further uses SVM on CNN feature embeddings modified on HMO to improve decision boundary sharpness. Robust performance is shown by experiments carried out on the ACS skin lesion dataset validated by 5-fold cross-valuation and ISIC 2018 benchmarks with an accuracy of 95.02% and consistent generalizing over folds. Crucially, significant parallelism potential made possible by the population-based structure of HMO makes the framework fit for GPU clusters or cloud-based training pipelines. Computational benchmarks expose reasonable overhead in trade for best performance. Thus, the suggested system is a strong contender for implementation in high-performance and distributed computing contexts since it provides both diagnostic dependability and computational tractability. [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: 194356961 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Scalable HMO-CNN-SVM Framework for Skin Lesion Classification: A Metaheuristic-Driven Approach With Parallelizable Optimization for Cluster Deployment. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Fendzi+Mbasso%2C+Wulfran%22">Fendzi Mbasso, Wulfran</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> fendzi.wulfran@yahoo.fr</i><br /><searchLink fieldCode="AR" term="%22Harrison%2C+Ambe%22">Harrison, Ambe</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> fendzi.wulfran@yahoo.fr</i><br /><searchLink fieldCode="AR" term="%22Mamadiyarov%2C+Zokir%22">Mamadiyarov, Zokir</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Liu%2C+Zhe%22">Liu, Zhe</searchLink><relatesTo>4</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Kumar%2C+Raman%22">Kumar, Raman</searchLink><relatesTo>5</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Shaikh%2C+Muhammad+Suhail%22">Shaikh, Muhammad Suhail</searchLink><relatesTo>6,7</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Metwally+Mahmoud%2C+Mohamed%22">Metwally Mahmoud, Mohamed</searchLink><relatesTo>8</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Biomedical+Engineering+%26+Computational+Biology%22">Biomedical Engineering & Computational Biology</searchLink>. 6/6/2026, Vol. 17, p1-20. 20p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Convolutional+neural+networks%22">Convolutional neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Metaheuristic+algorithms%22">Metaheuristic algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Computer-assisted+image+analysis+%28Medicine%29%22">Computer-assisted image analysis (Medicine)</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Parallel+programming%22">Parallel programming</searchLink><br /><searchLink fieldCode="DE" term="%22Skin+disease+diagnosis%22">Skin disease diagnosis</searchLink><br /><searchLink fieldCode="DE" term="%22Support+vector+machines%22">Support vector machines</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: In medical image analysis, accurate skin lesion categorization is still a major difficulty particularly under limited data conditions and computational complexity. For automated skin cancer detection, in this work we present a scalable hybrid model combining a Convolutional Neural Network (CNN), the Harmonic Mean Optimizer (HMO), and a Support Vector Machine (SVM) classifier—termed HMO-CNN-SVM. Key CNN hyperparameters including learning rate, batch size, and kernel configuration are optimized using the HMO, so greatly boosting classification performance over manual or stationary settings. The model further uses SVM on CNN feature embeddings modified on HMO to improve decision boundary sharpness. Robust performance is shown by experiments carried out on the ACS skin lesion dataset validated by 5-fold cross-valuation and ISIC 2018 benchmarks with an accuracy of 95.02% and consistent generalizing over folds. Crucially, significant parallelism potential made possible by the population-based structure of HMO makes the framework fit for GPU clusters or cloud-based training pipelines. Computational benchmarks expose reasonable overhead in trade for best performance. Thus, the suggested system is a strong contender for implementation in high-performance and distributed computing contexts since it provides both diagnostic dependability and computational tractability. [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: 10.1177/11795972261453621 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 20 StartPage: 1 Subjects: – SubjectFull: Convolutional neural networks Type: general – SubjectFull: Metaheuristic algorithms Type: general – SubjectFull: Computer-assisted image analysis (Medicine) Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Parallel programming Type: general – SubjectFull: Skin disease diagnosis Type: general – SubjectFull: Support vector machines Type: general Titles: – TitleFull: Scalable HMO-CNN-SVM Framework for Skin Lesion Classification: A Metaheuristic-Driven Approach With Parallelizable Optimization for Cluster Deployment. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Fendzi Mbasso, Wulfran – PersonEntity: Name: NameFull: Harrison, Ambe – PersonEntity: Name: NameFull: Mamadiyarov, Zokir – PersonEntity: Name: NameFull: Liu, Zhe – PersonEntity: Name: NameFull: Kumar, Raman – PersonEntity: Name: NameFull: Shaikh, Muhammad Suhail – PersonEntity: Name: NameFull: Metwally Mahmoud, Mohamed IsPartOfRelationships: – BibEntity: Dates: – D: 06 M: 06 Text: 6/6/2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 11795972 Numbering: – Type: volume Value: 17 Titles: – TitleFull: Biomedical Engineering & Computational Biology Type: main |
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