Department of Electronics and Communication Engineering, NPR College of Engineering & Technology, Dindigul, Tamil Nadu, India.
Department of Electronics and Communication Engineering, SRM Institute of Science and Technology, Ramapuram Campus, Chennai, Tamilnadu, India.
Microsc Res Tech. 2024 Aug;87(8):1742-1752. doi: 10.1002/jemt.24550. Epub 2024 Mar 19.
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.
本文提出了一种基于 PCSAN-Net-DBOA 的热图像算法。首先，从数据库中获取用于乳腺研究的输入图像，包括乳腺癌分类的红外图像（DMR-IR）数据集。使用自适应变形高斯匹配滤波器（ADGMF）去除噪声，提高红外热图像的质量。然后，将预处理后的图像输入一维量子整数小波 S 变换（OQIWST），提取灰度统计特征，如标准差、均值、方差、熵、峰度和偏度。提取的特征输入到金字塔卷积洗牌注意力神经网络（PCSANN）进行分类。一般来说，PCSANN 没有使用任何自适应优化技术来确定最佳参数，以提供精确的乳腺癌分类。本研究提出了 dung beetle 优化算法（DBOA）来优化 PCSANN 分类器，以准确诊断乳腺癌。BCD-PCSANN-DBO 方法使用 Python 实现。为了对乳腺癌进行分类，考虑了包括准确性、精度、召回率、F1 分数、误差率、ROC 和计算时间在内的性能指标。与现有的方法相比，如利用热红外成像和机器学习方法进行乳腺癌诊断的 BCD-CNN、利用 Grunwald-Letnikov 辅助蜻蜓算法的基于深度特征选择的热图像乳腺癌分类的 BCD-VGG16 以及利用基于遗传算法和灰狼优化器的深度选择的热谱图乳腺癌检测的 BCD-SqueezeNet 相比，所提出的 BCD-PCSANN-DBO 方法在计算时间上分别降低了 29.87%、28.95%和 27.92%，ROC 分别提高了 13.29%、14.35%和 20.54%。研究亮点：从乳腺癌数据集获取输入图像，进行乳腺癌分类。使用 ADQMF 去除噪声，提高红外热图像的质量。提取特征后输入 PCSANN 进行分类。提出了 DBOA 来优化精确分类乳腺癌的 PCSANN 分类器。使用 Python 实现了所提出的 BCD-PCSANN-DBO 方法。
