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. 2024 May 10;14(1):10714.
doi: 10.1038/s41598-024-61322-w.

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

Anas Bilal  1   2 , Azhar Imran  3 , Talha Imtiaz Baig  4 , Xiaowen Liu  1 , Emad Abouel Nasr  5 , Haixia Long  6   7
Affiliations

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

Anas Bilal et al. Sci Rep. .

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.

Keywords: Breast cancer; Grey wolf optimization; Medical image analysis; Quantum computing; Support vector machine.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
Breast images show benign masses (right) and malignant (left).
Figure 2
Figure 2
Flowchart of BGWO.
Figure 3
Figure 3
The framework of the proposed methodology.
Figure 4
Figure 4
MIAS breast mammogram images.
Figure 5
Figure 5
Preprocessing, ROI extraction: (a) original image (b) median filter (2) CLAHE (d) ROIs Extraction (e) ROI cropped (f) Extracted ROI patches to 120 × 120 pixels.
Figure 6
Figure 6
Flow chart of IQI-BGWO-SVM.
Figure 7
Figure 7
Analyzing the convergence patterns of IQI-BGWO on standard test functions.
Figure 8
Figure 8
A visual comparison of algorithmic efficiency across metrics.
Figure 8
Figure 8
A visual comparison of algorithmic efficiency across metrics.
Figure 9
Figure 9
ROC Curves of the BGWO-SVM and IQI-BGWO-SVM methods across different cross-validation settings.
Figure 10
Figure 10
Comparison of the MCC values for the BGWO-SVM and IQI-BGWO-SVM methods across different cross-validation settings.
See this image and copyright information in PMC

References

    1. Sung H, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: A Cancer J. Clin. 2021;71(3):209–249. doi: 10.3322/caac.21660. - DOI - PubMed
    1. Momenimovahed Z, Salehiniya H. Epidemiological characteristics of and risk factors for breast cancer in the world. Breast Cancer: Targets Ther. 2019;11:151. doi: 10.2147/BCTT.S176070. - DOI - PMC - PubMed
    1. Lopez, M. E., & Olutoye, O. O. Breast embryology, anatomy, and physiology. In: Endocrine Surgery in Children, (2017) doi: 10.1007/978-3-662-54256-9_27.
    1. Kretz T, Mueller KR, Schaeffter T, Elster C. Mammography image quality assurance using deep learning. IEEE Trans. Biomed. Eng. 2020;67(12):3317. doi: 10.1109/TBME.2020.2983539. - DOI - PubMed
    1. Murat Karabatak M, Ince C. An expert system for detection of breast cancer based on association rules and neural network. Expert Syst. Appl. 2009;36(2):3465–3469. doi: 10.1016/j.eswa.2008.02.064. - DOI

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