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

Xiaoyan Jiang, Zuojin Hu, Zhaozhao Xu
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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.

Abstract Image

CSO-CNN:用于乳腺癌移动检测的猫群优化引导的卷积神经网络
乳腺癌已成为世界上最常见的癌症。早期诊断和治疗可以大大提高乳腺癌患者的生存率。基于卷积神经网络(CNN)的计算机诊断技术可根据医学图像协助检测乳腺癌,有效提高检测准确率。卷积神经网络的超参数会影响模型的性能,因此在模型训练时需要对超参数进行调整。然而,传统的调整方法可能会陷入局部最小值。因此,通常使用全局优化算法来训练人工神经网络的权重和偏置。我们的研究引入了猫群优化(CSO),构建了猫群优化引导的卷积神经网络(CSO-CNN)。该模型能快速获得超参数的最优组合,并稳定地接近全局最优。统计结果表明,CSO-CNN 的灵敏度为 93.50% ± 2.42%,特异度为 92.20% ± 3.29%,精确度为 92.35% ± 3.01%,准确度为 92.85% ± 2.49%,F1 分数为 92.91% ± 2.44%,Matthews 相关系数为 85.74% ± 4.94%,Fowlkes-Mallows 指数为 92.92% ± 2.43%。我们的 CSO-CNN 算法优于五种最先进的方法。此外,我们还在本地计算机上测试了 CSO-CNN 算法,以模拟移动环境,并证实该算法可以移植到网络服务器上。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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