Department of Educational Technology, College of Education, Princess Nourah bint Abdulrahman University, Riyadh 11671, Saudi Arabia.
Applied College, Curriculum and Instruction, Princess Nourah bint Abdulrahman University, Riyadh 11671, Saudi Arabia.
Medicina (Kaunas). 2023 Mar 1;59(3):487. doi: 10.3390/medicina59030487.
: The classification of breast cancer is performed based on its histological subtypes using the degree of differentiation. However, there have been low levels of intra- and inter-observer agreement in the process. The use of convolutional neural networks (CNNs) in the field of radiology has shown potential in categorizing medical images, including the histological classification of malignant neoplasms. : This study aimed to use CNNs to develop an automated approach to aid in the histological classification of breast cancer, with a focus on improving accuracy, reproducibility, and reducing subjectivity and bias. The study identified regions of interest (ROIs), filtered images with low representation of tumor cells, and trained the CNN to classify the images. : The major contribution of this research was the application of CNNs as a machine learning technique for histologically classifying breast cancer using medical images. The study resulted in the development of a low-cost, portable, and easy-to-use AI model that can be used by healthcare professionals in remote areas. : This study aimed to use artificial neural networks to improve the accuracy and reproducibility of the process of histologically classifying breast cancer and reduce the subjectivity and bias that can be introduced by human observers. The results showed the potential for using CNNs in the development of an automated approach for the histological classification of breast cancer.
乳腺癌的分类是基于其组织学亚型，并使用分化程度来进行的。然而，在这个过程中，存在着观察者内和观察者间的低一致性。卷积神经网络（CNNs）在放射学领域的应用已经显示出在分类医学图像方面的潜力，包括恶性肿瘤的组织学分类。
本研究旨在使用 CNN 开发一种自动化方法来辅助乳腺癌的组织学分类，重点是提高准确性、可重复性，并减少主观性和偏见。该研究确定了感兴趣的区域（ROIs），过滤了肿瘤细胞代表性低的图像，并训练了 CNN 来对图像进行分类。
这项研究的主要贡献是将 CNN 作为一种机器学习技术应用于使用医学图像对乳腺癌进行组织学分类。该研究的结果是开发了一种低成本、便携式和易于使用的人工智能模型，可由偏远地区的医疗保健专业人员使用。
本研究旨在使用人工神经网络提高乳腺癌组织学分类过程的准确性和可重复性，并减少由人类观察者引入的主观性和偏见。结果表明，在开发乳腺癌组织学分类的自动化方法方面，使用 CNN 具有潜力。
