AI 中文总结
本研究通过公开胸部X线数据开展试点，用HOG、GLCM等特征结合经典分类器区分COVID-19与其他肺炎，为未来多模态深度学习架构研究提供方向。
AI 中文摘要
从胸部X光片自动分类肺部疾病是机器学习在医学成像领域的广泛研究应用。本试点研究使用公开的COVID-19图像数据集（含408名患者的668张后前位/前后位X光片），评估基于纹理和梯度的经典特征表示，以区分COVID-19与其他类型肺炎。采用方向梯度直方图（HOG）、灰度共生矩阵（GLCM）纹理描述子，结合经典分类器（逻辑回归、随机森林、支持向量机），在患者层面的5折分层交叉验证下评估（防止数据泄露），获得最佳平均准确率75.4%、AUC为0.755，略高于71.6%的多数类基线。本研究透明报告结果及局限性，以此为基础提出多模态深度学习架构（跨成像模态结合卷积与基于Transformer的编码器），作为未来需更大规模、多机构、符合伦理的数据集的研究方向。
英文摘要
Automated classification of pulmonary disease from chest radiographs is a widely studied application of machine learning in medical imaging. This paper presents a pilot study evaluating classical texture- and gradient-based feature representations for distinguishing COVID-19 from other forms of pneumonia using the publicly available COVID-19 Image Data Collection (668 posteroanterior/anteroposterior radiographs from 408 patients). Using histogram of oriented gradients (HOG) and gray-level co-occurrence matrix (GLCM) texture descriptors with classical classifiers (logistic regression, random forest, and support vector machine), evaluated under patient-level 5-fold stratified cross-validation to prevent data leakage, we obtain a best mean accuracy of 75.4% and AUC of 0.755, modestly exceeding the 71.6% majority-class baseline. We report these results transparently, including their limitations, and use them to motivate and scope a proposed multi-modal deep learning architecture -- combining convolutional and transformer-based encoders across imaging modalities -- as a direction for future work requiring access to larger, multi-institutional, ethically sourced datasets.
Comments6 pages, 2 figures
