This manuscript proposes thermal images using PCSAN‐Net‐DBOA Initially, the input images are engaged from the database for mastology research with infrared image (DMR‐IR) dataset for breast cancer classification. The adaptive distorted Gaussian matched‐filter (ADGMF) was used in removing noise and increasing the quality of infrared thermal images. Next, these preprocessed images are given into one‐dimensional quantum integer wavelet S‐transform (OQIWST) for extracting Grayscale statistic features like standard deviation, mean, variance, entropy, kurtosis, and skewness. The extracted features are given into the pyramidal convolution shuffle attention neural network (PCSANN) for categorization. In general, PCSANN does not show any adaption optimization techniques to determine the optimal parameter to offer precise breast cancer categorization. This research proposes the dung beetle optimization algorithm (DBOA) to optimize the PCSANN classifier that accurately diagnoses breast cancer. The BCD‐PCSANN‐DBO method is implemented using Python. To classify breast cancer, performance metrics including accuracy, precision, recall, F1 score, error rate, RoC, and computational time are considered. Performance of the BCD‐PCSANN‐DBO approach attains 29.87%, 28.95%, and 27.92% lower computation time and 13.29%, 14.35%, and 20.54% greater RoC compared with existing methods like breast cancer diagnosis utilizing thermal infrared imaging and machine learning approaches(BCD‐CNN), breast cancer classification from thermal images utilizing Grunwald‐Letnikov assisted dragonfly algorithm‐based deep feature selection (BCD‐VGG16) and Breast cancer detection in thermograms using deep selection based on genetic algorithm and Gray Wolf Optimizer (BCD‐SqueezeNet), respectively.Research Highlights The input images are engaged from the breast cancer dataset for breast cancer classification. The ADQMF was used in removing noise and increasing the quality of infrared thermal images. The extracted features are given into the PCSANN for categorization. DBOA is proposed to optimize PCSANN classifier that classifies breast cancer precisely. The proposed BCD‐PCSANN‐DBO method is implemented using Python.

中文翻译：

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使用 PCSAN-Net-DBOA 的红外热图像：一种乳腺癌分类方法

本手稿提出使用 PCSAN-Net-DBOA 的热图像最初，输入图像来自用于乳腺学研究的数据库，以及用于乳腺癌分类的红外图像 (DMR-IR) 数据集。自适应失真高斯匹配滤波器（ADGMF）用于消除噪声并提高红外热图像的质量。接下来，将这些预处理后的图像进行一维量子整数小波 S 变换（OQIWST），以提取标准差、均值、方差、熵、峰度和偏度等灰度统计特征。提取的特征被输入金字塔卷积洗牌注意力神经网络（PCSANN）进行分类。一般来说，PCSANN 没有展示任何适应优化技术来确定提供精确乳腺癌分类的最佳参数。本研究提出了粪甲虫优化算法（DBOA）来优化准确诊断乳腺癌的 PCSANN 分类器。 BCD-PCSANN-DBO方法是使用Python实现的。为了对乳腺癌进行分类，需要考虑的性能指标包括准确度、精确度、召回率、F1 分数、错误率、RoC 和计算时间。与利用热红外成像和机器学习方法进行乳腺癌诊断等现有方法相比，BCD-PCSANN-DBO 方法的计算时间缩短了 29.87%、28.95% 和 27.92%，RoC 提高了 13.29%、14.35% 和 20.54% (BCD-CNN)、利用 Grunwald-Letnikov 辅助蜻蜓算法的深度特征选择对热图像进行乳腺癌分类 (BCD-VGG16) 以及使用基于遗传算法和灰狼优化器的深度选择在热像图中检测乳腺癌 (BCD-SqueezeNet) ）分别。 研究亮点 输入图像来自乳腺癌数据集，用于乳腺癌分类。 ADQMF 用于消除噪声并提高红外热图像的质量。 提取的特征被输入PCSANN进行分类。 DBOA被提出来优化PCSANN分类器，以精确地对乳腺癌进行分类。 所提出的 BCD-PCSANN-DBO 方法是使用 Python 实现的。