Stacked Ensemble of Fine-Tuned CNNs for Knee Osteoarthritis Severity Grading
This work addresses diagnostic inaccuracies and time constraints in medical imaging for older individuals, but it is incremental as it builds on existing CNN and ensemble techniques.
The paper tackled the problem of grading knee osteoarthritis severity from X-ray images by developing a stacked ensemble of fine-tuned CNNs, achieving 73% accuracy for multiclass grading and 87.5% for binary detection, outperforming prior methods.
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.