Breast cancer is the second deadliest type of cancer. Early detection of breast cancer can considerably improve the effectiveness of treatment. A significant early sign of breast cancer is the mass. However, separating the cancerous masses from the normal portions of the breast tissue is usually a challenge for radiologists. Recently, because of the availability of high‐accuracy computing, computer‐aided detection systems based on image processing have become capable of accurately diagnosing the various types of cancers. The main purpose of this study is to utilize a powerful image segmentation method for the diagnosis of cancerous regions through mammography, based on a new configuration of the multilayer perceptron (MLP) neural network. The most popular method for minimizing the errors in an MLP neural network is backpropagation. However, this method has certain drawbacks, such as a low convergence speed and becoming trapped at the local minimum. In this study, a new training algorithm based on the whale optimization algorithm is proposed for the MLP network. This algorithm is capable of solving various problems toward the current algorithms for the analyzed systems. The proposed method is validated on the Mammographic Image Analysis Society database, which contains 322 digitized mammography images, and the Digital Database for Screening Mammography, which contains approximately 2500 digitized mammography images. To assess the detection performance of the proposed system, the correct detection rate, percentage of identification with false acceptance, and percentage of identification with false rejection were evaluated and compared using various methods. The results indicate that the proposed method is highly efficient and yields significantly better accuracy compared with other methods.

中文翻译：

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基于鲸鱼优化算法的优化神经网络的乳腺癌自动检测

乳腺癌是第二大致命癌症。早期发现乳腺癌可以大大提高治疗效果。肿块是乳腺癌的重要早期征兆。然而，将癌肿与乳房组织的正常部分分开通常是放射科医生的挑战。近年来，由于高精度计算的可用性，基于图像处理的计算机辅助检测系统已经能够准确诊断各种类型的癌症。这项研究的主要目的是基于多层感知器（MLP）神经网络的新配置，利用功能强大的图像分割方法通过乳腺X线摄影诊断癌症区域。最小化MLP神经网络中的错误的最流行方法是反向传播。然而，该方法具有某些缺点，例如收敛速度低并且陷入局部最小值。在这项研究中，针对鲸鱼网络提出了一种基于鲸鱼优化算法的训练算法。该算法能够针对被分析系统的当前算法解决各种问题。该方法已在包含322幅数字化乳腺X线照片的乳房X线图像分析协会数据库以及包含大约2500幅数字化X线乳腺图像的数字化筛查乳房X线照片数据库中得到了验证。为了评估所提出系统的检测性能，使用各种方法评估并比较了正确的检测率，接受错误识别的百分比和接受错误拒绝的百分比。