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arXiv · 2511.22143

Stacked Ensemble of Fine-Tuned CNNs for Knee Osteoarthritis Severity Grading

Adarsh Gupta·Japleen Kaur·Tanvi Doshi·Teena Sharma·Nishchal K. Verma·Shantaram Vasikarla
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Abstract

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.

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Adarsh Gupta, Japleen Kaur, Tanvi Doshi, Teena Sharma, Nishchal K. Verma, Shantaram Vasikarla. 2025-11-27. Stacked Ensemble of Fine-Tuned CNNs for Knee Osteoarthritis Severity Grading. https://arxiv.org/abs/2511.22143

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