用多模型集成提升膝骨关节炎分级准确率
Stacked Ensemble of Fine-Tuned CNNs for Knee Osteoarthritis Severity Grading
- 融合MobileNetV2、YOLOv8等预训练CNN作为基学习器，用CatBoost做元学习器
- 多分类准确率达73%，二分类达87.5%，优于已有方法
- 适合医学影像分析与自动化诊断场景
膝骨关节炎（KOA）是一种常见退行性关节病，常导致老年人日常活动受限。目前临床通过膝关节X光片按Kellgren-Lawrence（KL）分级系统（0-4级）评估严重程度，但依赖专家经验，耗时且易受主观判断影响。为此，本文提出一种微调后的卷积神经网络堆叠集成模型，用于两个任务：检测是否存在KOA（二分类）和精确分级（多分类）。该模型以MobileNetV2、YOLOv8、DenseNet201为基学习器，使用Categorical Boosting（CatBoost）作为元学习器。在测试集上，多分类平衡准确率为73%，二分类准确率为87.5%，高于现有文献成果。
原文摘要 · Abstract (English)
Knee Osteoarthritis (KOA) is a musculoskeletal condition that can cause significant limitations and impairments in daily activities, especially among older individuals. To evaluate the severity of KOA, typically, X-ray images of the affected knee are analyzed, and a grade is assigned based on the Kellgren-Lawrence (KL) grading system, which classifies KOA severity into five levels, ranging from 0 to 4. This approach requires a high level of expertise and time and is susceptible to subjective interpretation, thereby introducing potential diagnostic inaccuracies. To address this problem a stacked ensemble model of fine-tuned Convolutional Neural Networks (CNNs) was developed for two classification tasks: a binary classifier for detecting the presence of KOA, and a multiclass classifier for precise grading across the KL spectrum. The proposed stacked ensemble model consists of a diverse set of pre-trained architectures, including MobileNetV2, You Only Look Once (YOLOv8), and DenseNet201 as base learners and Categorical Boosting (CatBoost) as the meta-learner. This proposed model had a balanced test accuracy of 73% in multiclass classification and 87.5% in binary classification, which is higher than previous works in extant literature.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。