Abstract
Early screening of cancer is crucial for patients’ survival time and quality of life. Based on this, this essay suggests a tumor cell detection technique based on a combination of whale optimization technique is proposed and support vector machine. Firstly, tumor cells are categorized using the support vector machine technique; secondly, the support vector machine's parameters have been optimized using the whale optimization technique to continue increasing the model’s accuracy; eventually, a variety of approaches are dreamed up for comparison in order to confirm the success of the suggested approach. The program's findings demonstrate that the previously proposed model's accuracy is around 5% better than that of the conventional support vector machine training regimen, demonstrating the proposed model's cost effectiveness in the field of breast cancer cell proof of identity.
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References
Krithiga, R., Geetha, P.: Deep learning based breast cancer detection and classification using fuzzy merging techniques. Mach. Vis. Appl. 31(7–8) (2020). https://doi.org/10.1007/s00138-020-01122-0
Hu, H., et al.: Machine learning for identification of apoptotic breast cancer cells based on 3D morphological parameters. Sci. Technol. Innov. 20, 117–118 (2020)
Han, H., Wei, B., Sui, D., Li, S.: A U-Net-Based method for detection of cancer cells in pathological sections of breast cancer. Precision Clin. Med. 33(06), 471–473+477 (2018)
Yang, X., Wang, Z., Li, J.: Research cancer cell recognition system based on deep neural network. Software Guide 19(03), 65–68 (2020)
Pan, A., Xu, S., Cheng, Y., She, Y.: Breast mass image recognition based on SVGG16. J. South-Central Univ. Natl. (Natl. Sci. Edn.) 40(04), 410–416 (2021)
Lai, S., Liu, Q., Yu, L., Liu, W., Yang, R., Jin, H.: Construction of breast cancer prediction model based on SFS-SVM. Chin. J. Med. Phys. 36(07), 826–829 (2019)
Li, H.: BBO-based optimized BP neural network for breast cancer diagnosis. Shanxi Electron. Technol. (5), 35–36, 44 (2018)
Li, J., He, R.: Research on breast cancer detection method based on a deep forest algorithm. New Gener. Technol. 8, 11–16 (2021)
Yu, Y., Fan, C., Zhu, R., Xiong, H.: Breast cancer diagnosis based on combined H-SVM model. Intell. Comput. Appl. 10(11), 97–100+105 (2020)
Wang, D., Huang, Y.: Prediction of breast cancer based on SVM-MLP. Microcomput. Appl. 38(01), 130–133+138 (2022)
Vapnik, V.: The nature of statistical learning theory. Springer science&. Business media (2013)
Mirjalili, S., Lewis, A.: The whale optimization algorithm. Adv. Eng. Softw. 95, 51–67 (2016)
Tharwat, A., Moemen, Y.S., Hassanien, A.E.: Classification of toxicity effect sof biotransformed hepatic drugs using whale optimized support vector machines. J. Biomed. Inform. 68, 132–149 (2017)
Handelman, G.S., Kok, H.K., Chandra, R.V., et al.: Peering into the black box ofartificial intelligence: evaluation metrics of machine learning methods. Am. J. Roentgenol. 212(1), 38–43 (2019)
Chang, C.C., Lin, C.J., LIBSVM: A library for support vector machines. ACM Trans. Intell. Syst. Technol. 2(3), 27 (2011)
Oskouei, R.J., Kor, N.M., Maleki, S.A.: Data mining and medical world: breast cancers’ diagnosis, treatment, prognosis and challenges. Am. J. Cancer Res. 7(3), 610–627 (2017)
Liang, Z., Li, Z., Lai, J.: Application of 10-fold cross-validation in the evaluation of generalization ability of prediction models and the realization in R. Chin. J. Hospital Stat. 27(04), 289–292 (2020)
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Luo, Z., Li, Y., Luo, D., Wang, L., Zhu, J. (2023). Application of WOA-SVM Based Algorithm in Tumor Cell Detection Research. In: Zhang, H., et al. International Conference on Neural Computing for Advanced Applications. NCAA 2023. Communications in Computer and Information Science, vol 1869. Springer, Singapore. https://doi.org/10.1007/978-981-99-5844-3_9
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DOI: https://doi.org/10.1007/978-981-99-5844-3_9
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