乳腺癌是全世界女性的主要癌症原因之一。它可分为浸润性导管癌（IDC）或转移性癌症。由于缺乏早期预警信号，乳腺癌的早期发现具有挑战性。一般来说，专家建议进行乳房X光检查进行筛查。现有方法对于实时诊断应用来说不够准确，因此需要更好、更智能的癌症诊断方法。本研究旨在开发一种定制的机器学习框架，为 IDC 和转移性癌症分类提供更准确的预测。这项工作提出了一种用于对 IDC 和转移性乳腺癌进行分类的卷积神经网络 (CNN) 模型。该研究利用了大规模的显微组织病理学图像数据集来自动感知学习和理解的分层方式。很明显，使用机器学习技术显着（15%-25%）提高了确定癌症易损性、恶性肿瘤和癌症的有效性。灭亡。结果表明，该模型具有出色的性能，可确保将转移性细胞与良性细胞分类的平均准确率达到 95%，在检测 IDC 方面获得 89% 的准确率。结果表明，所提出的模型提高了分类准确率。因此，与其他最先进的模型相比，它可以有效地应用于 IDC 和转移性癌症的分类。© 2024 作者。约翰·威利出版的癌症医学

Breast cancer is one of the leading cancer causes among women worldwide. It can be classified as invasive ductal carcinoma (IDC) or metastatic cancer. Early detection of breast cancer is challenging due to the lack of early warning signs. Generally, a mammogram is recommended by specialists for screening. Existing approaches are not accurate enough for real-time diagnostic applications and thus require better and smarter cancer diagnostic approaches. This study aims to develop a customized machine-learning framework that will give more accurate predictions for IDC and metastasis cancer classification.This work proposes a convolutional neural network (CNN) model for classifying IDC and metastatic breast cancer. The study utilized a large-scale dataset of microscopic histopathological images to automatically perceive a hierarchical manner of learning and understanding.It is evident that using machine learning techniques significantly (15%-25%) boost the effectiveness of determining cancer vulnerability, malignancy, and demise. The results demonstrate an excellent performance ensuring an average of 95% accuracy in classifying metastatic cells against benign ones and 89% accuracy was obtained in terms of detecting IDC.The results suggest that the proposed model improves classification accuracy. Therefore, it could be applied effectively in classifying IDC and metastatic cancer in comparison to other state-of-the-art models.© 2024 The Author(s). Cancer Medicine published by John Wiley & Sons Ltd.