利用组织病理学图像自动进行肺癌和结肠癌分类

IF 2.3 Q3 ENGINEERING, BIOMEDICAL
Biomedical Engineering and Computational Biology Pub Date : 2024-08-14 eCollection Date: 2024-01-01 DOI:10.1177/11795972241271569
Jie Ji, Jirui Li, Weifeng Zhang, Yiqun Geng, Yuejiao Dong, Jiexiong Huang, Liangli Hong
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引用次数: 0

摘要

癌症是世界上最主要的死亡原因。而在所有癌症中,肺癌和结肠癌是最常见的两种致死和发病原因。本研究的目的是利用组织病理学图像开发一个自动肺癌和结肠癌分类系统。研究人员利用 LC25000 数据集中的组织病理学图像开发了一套自动肺癌和结肠癌分类系统。算法开发包括数据分割、深度神经网络模型选择、实时图像增强、训练和验证。算法的核心是 Swin Transform V2 模型,并使用 5 倍交叉验证来评估模型性能。模型性能使用准确度、Kappa、混淆矩阵、精确度、召回率和 F1 进行评估。为了比较不同神经网络(包括主流卷积神经网络和视觉转换器)的性能,我们进行了广泛的实验。Swin Transform V2 模型在所有指标上都达到了 1(100%),是首个在该数据集上获得完美结果的单一模型。Swin Transformer V2 模型有望用于协助病理学家利用组织病理学图像对肺癌和结肠癌进行分类。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Automated Lung and Colon Cancer Classification Using Histopathological Images.

Cancer is the leading cause of mortality in the world. And among all cancers lung and colon cancers are 2 of the most common causes of death and morbidity. The aim of this study was to develop an automated lung and colon cancer classification system using histopathological images. An automated lung and colon classification system was developed using histopathological images from the LC25000 dataset. The algorithm development included data splitting, deep neural network model selection, on the fly image augmentation, training and validation. The core of the algorithm was a Swin Transform V2 model, and 5-fold cross validation was used to evaluate model performance. The model performance was evaluated using Accuracy, Kappa, confusion matrix, precision, recall, and F1. Extensive experiments were conducted to compare the performances of different neural networks including both mainstream convolutional neural networks and vision transformers. The Swin Transform V2 model achieved a 1 (100%) on all metrics, which is the first single model to obtain perfect results on this dataset. The Swin Transformer V2 model has the potential to be used to assist pathologists in classifying lung and colon cancers using histopathology images.

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