Comparison of Feature Selection Methods and Machine Learning Classifiers with Computed Tomography Radiomics-based Features for Predicting Chronic Obstructive Pulmonary Disease

特征选择方法和机器学习分类器与基于计算机断层扫描放射组学特征的预测慢性阻塞性肺疾病的比较

• 批准号: 466971
• 负责人: Makimoto Kalysta M
• 金额: $ 1.27万
• 依托单位: Toronto Metropolitan University
• 依托单位国家: 加拿大
• 项目类别: Studentship Programs
• 财政年份: 2021
• 资助国家: 加拿大
• 起止时间: 2021-12-01 至 2022-12-01
• 项目状态: 已结题
• 来源: https://webapps.cihr-irsc.gc.ca/decisions/p/project_details.html?applId=466971&lang=en
• 关键词: Biophysics; Biostatistics; Chronic Obstructive Lung Disease; Classification; Feature Selection; Imaging; Machine Learning; Radiomics; Respiration; Texture-Based Ct Features

Radiomics is an image analysis technique that extracts numerical features from medical images, for example computed tomography (CT) images. These radiomic features can be used with machine learning models to predict outcomes. Machine learning models inclu

放射组学是从医学图像（例如计算机断层摄影（CT）图像）提取数值特征的图像分析技术。这些放射组学特征可以与机器学习模型一起使用来预测结果。机器学习模型包括