基于深度学习的乳腺癌组织病理图像分类模型

Shubham Kushwaha, Mohammad Adil, M. Abuzar, Akib Nazeer, S. Singh
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引用次数: 3

摘要

乳腺癌是印度和世界上最常见的癌症。根据全球癌症观察站的数据,在2020年,仅这种癌症就导致了全球680多万女性的死亡。乳腺癌没有单一的病因,但它可能是一个人的环境、基因和生活方式的结合。这些乳腺肿瘤可为良性(非癌性)或恶性(潜在癌性)。因此,正确识别它们以进行适当的治疗变得至关重要。虽然组织病理学图像被用来判断乳腺癌,但总是有人为错误的机会。本文旨在为每个人简化乳腺癌的分类过程。我们提出了一种基于深度学习的卷积神经网络模型,利用组织病理学图像对乳腺癌进行检测和分类。该方法使用预训练的神经网络DenseNet-201从图像中提取特征,然后将其用于预测分类。我们的模型达到了97.05%的准确率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Deep learning-based model for breast cancer histopathology image classification
Breast cancer is the most commonly found cancer in India and the world. As per Global Cancer Observatory, in 2020 this cancer alone was the reason for the death of more than 6.8 million women throughout the world. There is no single cause of breast cancer but it could be a combination of one's environment, one's genes and the way one lives her life. These breast tumors can be benign (non- cancerous) or malignant (potentially-cancerous). Therefore, it becomes essential to identify them properly for appropriate treatment. Although histopathology images are used to judge breast cancer, there is always a chance of human error. This paper aims to simplify the breast cancer classification process for everyone. We propose a deep learning based Convolutional Neural Network model to detect and classify the breast cancer using histopathology images. This method uses pretrained neural network DenseNet-201 for features extraction from the images, which then used to predict for classification. Our model reached an accuracy of 97.05%.
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