Ji Jie, Li Jirui, Zhang Weifeng, Geng Yiqun, Dong Yuejiao, Huang Jiexiong, Hong Liangli
Network & Information Center, Shantou University, Shantou, Guangdong, China.
Guangdong Provincial International Collaborative Center of Molecular Medicine, Laboratory of Molecular Pathology, Shantou University Medical College, Shantou, China.
Biomed Eng Comput Biol. 2024 Aug 14;15:11795972241271569. doi: 10.1177/11795972241271569. eCollection 2024.
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.
癌症是全球主要的死亡原因。在所有癌症中，肺癌和结肠癌是导致死亡和发病的两个最常见原因。本研究的目的是利用组织病理学图像开发一种自动肺癌和结肠癌分类系统。使用来自LC25000数据集的组织病理学图像开发了一种自动肺癌和结肠癌分类系统。算法开发包括数据拆分、深度神经网络模型选择、实时图像增强、训练和验证。该算法的核心是Swin Transform V2模型，并使用5折交叉验证来评估模型性能。使用准确率、卡帕值、混淆矩阵、精确率、召回率和F1来评估模型性能。进行了广泛的实验，以比较不同神经网络的性能，包括主流卷积神经网络和视觉Transformer。Swin Transform V2模型在所有指标上都达到了1（100%），这是第一个在该数据集上获得完美结果的单一模型。Swin Transformer V2模型有潜力用于协助病理学家使用组织病理学图像对肺癌和结肠癌进行分类。
