Department of Biomedical Engineering, Faculty of Electrical and Mechanical Engineering, Damascus University, Damascus, Syria.
Faculty of Biomedical Engineering, Al-Andalus University for Medical Sciences, Tartous, Syria.
Sci Rep. 2024 Sep 27;14(1):22215. doi: 10.1038/s41598-024-73083-7.
Breast cancer (BC) is a prominent cause of female mortality on a global scale. Recently, there has been growing interest in utilizing blood and tissue-based biomarkers to detect and diagnose BC, as this method offers a non-invasive approach. To improve the classification and prediction of BC using large biomarker datasets, several machine-learning techniques have been proposed. In this paper, we present a multi-stage approach that consists of computing new features and then sorting them into an input image for the ResNet50 neural network. The method involves transforming the original values into normalized values based on their membership in the Gaussian distribution of healthy and BC samples of each feature. To test the effectiveness of our proposed approach, we employed the Coimbra and Wisconsin datasets. The results demonstrate efficient performance improvement, with an accuracy of 100% and 100% using the Coimbra and Wisconsin datasets, respectively. Furthermore, the comparison with existing literature validates the reliability and effectiveness of our methodology, where the normalized value can reduce the misclassified samples of ML techniques because of its generality.
乳腺癌（BC）是全球范围内女性死亡的主要原因。最近，人们越来越感兴趣地利用血液和组织生物标志物来检测和诊断 BC，因为这种方法是一种非侵入性的方法。为了利用大型生物标志物数据集改善 BC 的分类和预测，已经提出了几种机器学习技术。在本文中，我们提出了一种多阶段的方法，包括计算新特征，然后将其排序到 ResNet50 神经网络的输入图像中。该方法涉及根据每个特征的健康和 BC 样本的高斯分布成员将原始值转换为归一化值。为了测试我们提出的方法的有效性，我们使用了 Coimbra 和 Wisconsin 数据集。结果表明，性能得到了有效提高，Coimbra 和 Wisconsin 数据集的准确率分别达到 100%和 100%。此外，与现有文献的比较验证了我们的方法的可靠性和有效性，因为归一化值因其通用性可以减少 ML 技术的误分类样本。
