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CSO-CNN: Cat Swarm Optimization-guided Convolutional Neural Network for Mobile Detection of Breast Cancer

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Abstract

Breast cancer has become the most common cancer in the world. Early diagnosis and treatment can greatly improve the survival rate of breast cancer patients. Computer diagnostic technology based on convolutional neural networks (CNNs) can assist in detecting breast cancer based on medical images, effectively improving detection accuracy. Hyperparameters in CNN will affect model performance, so hyperparameter tuning is necessary for model training. However, traditional tuning methods can get stuck in local minimums. Therefore, the weights and biases of artificial neural networks are usually trained using global optimization algorithms. Our research introduces cat swarm optimization (CSO) to construct a cat swarm optimization-guided convolutional neural network (CSO-CNN). The model can quickly obtain the optimal combination of hyperparameters and stably get closer to the global optimal. The statistical results of CSO-CNN obtained a sensitivity of 93.50% ± 2.42%, a specificity of 92.20% ± 3.29%, a precision of 92.35% ± 3.01%, an accuracy of 92.85% ± 2.49%, an F1-score of 92.91% ± 2.44%, Matthews correlation coefficient of 85.74% ± 4.94%, and Fowlkes-Mallows index was 92.92% ± 2.43%. Our CSO-CNN algorithm is superior to five state-of-the-art methods. In addition, we tested the CSO-CNN algorithm on the local computer to simulate the mobile environment and confirmed that the algorithm can be transplanted to the network servers.

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Data Availability

Data available on reasonable request to corresponding authors.

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Acknowledgements

This paper is partially supported by National Natural Science Foundation of China (62303167); the Nationally Funded Postdoctoral Researcher Program of China (GZC20230707); Major project of philosophy and social science research in colleges and universities in Jiangsu Province, China (2023SJZD125).

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Authors and Affiliations

  1. School of Mathematics and Information Science, Nanjing Normal University of Special Education, Nanjing, 210038, China

    Xiaoyan Jiang & Zuojin Hu

  2. School of Computer Science and Technology, Henan Polytechnic University, Jiaozuo, 454000, Henan, China

    Zhaozhao Xu

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  1. Xiaoyan Jiang
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Contributions

Xiaoyan Jiang: Conceptualization, Methodology, Software, Formal analysis, Resources, Writing - Original Draft, Visualization. Zuojin Hu: Methodology, Validation, Formal analysis, Investigation, Writing - Review & Editing, Visualization, Project administration. ZhaoZhao Xu: Validation, Investigation, Data Curation, Writing - Review & Editing, Supervision, Project administration. All authors reviewed the manuscript

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Correspondence to Xiaoyan Jiang.

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Jiang, X., Hu, Z. & Xu, Z. CSO-CNN: Cat Swarm Optimization-guided Convolutional Neural Network for Mobile Detection of Breast Cancer. Mobile Netw Appl (2024). https://doi.org/10.1007/s11036-024-02298-9

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