Biomedical Engineering Program, Electrical and Computer Engineering Department, Faculty of Engineering, Beirut Arab University, Debbieh P.O. Box 11-5020, Lebanon.
Centre de Recherche du Centre Hospitalier, l'Université de Montréal, Montréal, QC H2X 0A9, Canada.
Sensors (Basel). 2024 Apr 5;24(7):2312. doi: 10.3390/s24072312.
Breast cancer results from a disruption of certain cells in breast tissue that undergo uncontrolled growth and cell division. These cells most often accumulate and form a lump called a tumor, which may be benign (non-cancerous) or malignant (cancerous). Malignant tumors can spread quickly throughout the body, forming tumors in other areas, which is called metastasis. Standard screening techniques are insufficient in the case of metastasis; therefore, new and advanced techniques based on artificial intelligence (AI), machine learning, and regression models have been introduced, the primary aim of which is to automatically diagnose breast cancer through the use of advanced techniques, classifiers, and real images. Real fine-needle aspiration (FNA) images were collected from Wisconsin, and four classifiers were used, including three machine learning models and one regression model: the support vector machine (SVM), naive Bayes (NB), k-nearest neighbors (k-NN), and decision tree (DT)-C4.5. According to the accuracy, sensitivity, and specificity results, the SVM algorithm had the best performance; it was the most powerful computational classifier with a 97.13% accuracy and 97.5% specificity. It also had around a 96% sensitivity for the diagnosis of breast cancer, unlike the models used for comparison, thereby providing an exact diagnosis on the one hand and a clear classification between benign and malignant tumors on the other hand. As a future research prospect, more algorithms and combinations of features can be considered for the precise, rapid, and effective classification and diagnosis of breast cancer images for imperative decisions.
乳腺癌是由于乳腺组织中某些细胞的失控生长和细胞分裂而导致的。这些细胞通常会聚集并形成一个称为肿瘤的肿块，这个肿瘤可能是良性（非癌性）的，也可能是恶性（癌性）的。恶性肿瘤可以迅速扩散到全身，在其他部位形成肿瘤，这被称为转移。在转移的情况下，标准的筛查技术是不够的；因此，已经引入了基于人工智能（AI）、机器学习和回归模型的新的先进技术，其主要目的是通过使用先进的技术、分类器和真实的图像自动诊断乳腺癌。从威斯康星州收集了真实的细针抽吸（FNA）图像，并使用了四种分类器，包括三种机器学习模型和一种回归模型：支持向量机（SVM）、朴素贝叶斯（NB）、k-最近邻（k-NN）和决策树（DT）-C4.5。根据准确率、敏感度和特异性结果，SVM 算法的性能最佳；它是最强大的计算分类器，准确率为 97.13%，特异性为 97.5%。它对乳腺癌的诊断也有大约 96%的敏感度，与用于比较的模型不同，因此一方面提供了准确的诊断，另一方面对良性和恶性肿瘤进行了明确的分类。作为未来的研究前景，可以考虑更多的算法和特征组合，以实现对乳腺癌图像的精确、快速和有效的分类和诊断，从而做出必要的决策。
