乳腺癌对全世界女性构成重大健康风险，在美国每年约有 30% 的女性被诊断出乳腺癌。在手术过程中识别癌性乳腺组织与非癌性乳腺组织对于彻底切除肿瘤至关重要。我们的研究创新地利用了机器学习技术（随机森林（RF）、支持向量机（SVM）和卷积神经网络（CNN）） ）与拉曼光谱一起简化和加速小鼠正常和晚期癌性乳腺组织的分化。这些模型的分类准确率分别为 RF 94.47%、SVM 96.76% 和 CNN 97.58%。据我们所知，这项研究是首次比较这三种机器学习技术根据拉曼光谱对乳腺癌组织进行分类的有效性。此外，我们创新性地识别了有助于小鼠癌组织和非癌组织分子特征的特定光谱峰。因此，我们的机器学习和拉曼光谱的综合方法为乳腺癌提供了一种非侵入性、快速的诊断工具，为乳腺癌提供了有前途的诊断工具。术中设置中的应用。© 2024。作者。

Breast cancer poses a significant health risk to women worldwide, with approximately 30% being diagnosed annually in the United States. The identification of cancerous mammary tissues from non-cancerous ones during surgery is crucial for the complete removal of tumors.Our study innovatively utilized machine learning techniques (Random Forest (RF), Support Vector Machine (SVM), and Convolutional Neural Network (CNN)) alongside Raman spectroscopy to streamline and hasten the differentiation of normal and late-stage cancerous mammary tissues in mice. The classification accuracy rates achieved by these models were 94.47% for RF, 96.76% for SVM, and 97.58% for CNN, respectively. To our best knowledge, this study was the first effort in comparing the effectiveness of these three machine-learning techniques in classifying breast cancer tissues based on their Raman spectra. Moreover, we innovatively identified specific spectral peaks that contribute to the molecular characteristics of the murine cancerous and non-cancerous tissues.Consequently, our integrated approach of machine learning and Raman spectroscopy presents a non-invasive, swift diagnostic tool for breast cancer, offering promising applications in intraoperative settings.© 2024. The Author(s).