Cited by 97AI in cancer detectionMetaheuristic Optimization Algorithms ResearchArtificial Intelligence in Healthcare

Breast cancer diagnosis using support vector machine optimized by improved quantum inspired grey wolf optimization

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Bilal, Anas, Imran, Azhar, Baig, Talha Imtiaz, Liu, Xiaowen, Abouel Nasr, Emad, Long, Haixia·2024·Scientific Reports

Abstract

A prompt diagnosis of breast cancer in its earliest phases is necessary for effective treatment. While Computer-Aided Diagnosis systems play a crucial role in automated mammography image processing, interpretation, grading, and early detection of breast cancer, existing approaches face limitations in achieving optimal accuracy. This study addresses these limitations by hybridizing the improved quantum-inspired binary Grey Wolf Optimizer with the Support Vector Machines Radial Basis Function Kernel. This hybrid approach aims to enhance the accuracy of breast cancer classification by determining the optimal Support Vector Machine parameters. The motivation for this hybridization lies in the need for improved classification performance compared to existing optimizers such as Particle Swarm Optimization and Genetic Algorithm. Evaluate the efficacy of the proposed IQI-BGWO-SVM approach on the MIAS dataset, considering various metric parameters, including accuracy, sensitivity, and specificity. Furthermore, the application of IQI-BGWO-SVM for feature selection will be explored, and the results will be compared. Experimental findings demonstrate that the suggested IQI-BGWO-SVM technique outperforms state-of-the-art classification methods on the MIAS dataset, with a resulting mean accuracy, sensitivity, and specificity of 99.25%, 98.96%, and 100%, respectively, using a tenfold cross-validation datasets partition.

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    From Multi-Sensing Monitoring of the Microalgae Biomass Cultivation Systems for Biofuels and Added Value Products Synthesis—Challenges and Opportunities (2025) · cites this paper as [244]

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Authors
Bilal, A., Imran, A., Baig, T. I., Liu, X., Abouel Nasr, E., & Long, H.
Affiliations
  • Hainan Normal University (CN)
  • Air University (PK)
  • University of Electronic Science and Technology of China (CN)
Corresponding author
Long, Haixia
Venue
Scientific Reports
Published
2024-05-10
Volume
14
Issue
1
Pages
10714-10714
ISSN
2045-2322 (ISSN-L)
OpenAlex
W4396805571
Cited by
97
References
72
Citations by year
2026: 25 · 2025: 51 · 2024: 21
FWCI
14.31
Type
journal article
Language
en
Open access
Locations
  • Scientific Reports (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
  • Greater South Information System
Funders
  • Hainan Provincial Natural Science Foundation of China ((No.621RC1059, 621MS038, 823RC488, 724RC510, 721QN0890))
  • King Saud University Researchers Supporting Program (RSP2024R164)
  • National Natural Science Foundation of China (62262019)
  • The Education Department of Hainan Province of China (No. Hnky2021-24)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
  • Physical Sciences › Computer Science › Artificial Intelligence › Metaheuristic Optimization Algorithms Research
  • Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
Keywords
breast cancer diagnosis, computer-aided diagnosis, support vector machine, Gaussian kernel, hyperparameter optimization, feature selection, MIAS dataset, hybrid optimization algorithm
MeSH terms
Algorithms, Breast Neoplasms, Diagnosis, Computer-Assisted, Female, Humans, Mammography, Support Vector Machine

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