Recently, image processing schemes are widely used to improve disease detection performance in many medicinal fields. Cancer is considered as one of the most deadly disease and early diagnosis of cancer is the complicated task in the field of medicine. In this paper, we present the two pretrained convolutional neural network (CNN) based on ensemble models such as VGG19 and VGG16 for cancer diagnosis that classifies both normal and abnormal images. The dilemma associated with CNN hyperparameter tuning complicates while diagnosing cancer. Hence, we propose multistrategy based artificial electric field (M-AEF) algorithm for hyper-parameter tuning in CNN thereby finding the optimal values. The exponentially decaying learning rates are more helpful to train CNN and prevent it from a local minimum. Thus, random minority over-sampling and random majority under-sampling address the imbalanced issue present in the dataset. The images are obtained from three different datasets namely the Kaggle dataset, International Collaboration on Cancer Reporting (ICCR) dataset, and cancer programming dataset for cancer detection. The experimental results are executed in MATLAB software and various performance analyses are carried out. Finally, the proposed method demonstrated better and higher cancer detection performance than other methods.

中文翻译：

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使用多策略人工电场算法优化的卷积神经网络进行癌症检测

最近，图像处理方案被广泛用于提高许多医学领域的疾病检测性能。癌症被认为是最致命的疾病之一，癌症的早期诊断是医学领域的一项复杂任务。在本文中，我们提出了两个基于集成模型（如 VGG19 和 VGG16）的预训练卷积神经网络 (CNN)，用于癌症诊断，可对正常和异常图像进行分类。与 CNN 超参数调整相关的困境在诊断癌症时变得复杂。因此，我们提出了基于多策略的人工电场 (M-AEF) 算法，用于 CNN 中的超参数调整，从而找到最佳值。指数衰减的学习率更有助于训练 CNN 并防止其出现局部最小值。因此，随机少数过采样和随机多数欠采样解决了数据集中存在的不平衡问题。这些图像来自三个不同的数据集，即 Kaggle 数据集、国际癌症报告合作组织 (ICCR) 数据集和用于癌症检测的癌症编程数据集。实验结果在MATLAB软件中执行，并进行了各种性能分析。最后，所提出的方法表现出比其他方法更好和更高的癌症检测性能。实验结果在MATLAB软件中执行，并进行了各种性能分析。最后，所提出的方法表现出比其他方法更好和更高的癌症检测性能。实验结果在MATLAB软件中执行，并进行了各种性能分析。最后，所提出的方法表现出比其他方法更好和更高的癌症检测性能。