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

TL;DR

This paper introduces a stacked ensemble of fine-tuned CNNs combined with a meta-learner to improve the accuracy of knee osteoarthritis severity grading from X-ray images, outperforming previous methods.

Contribution

It proposes a novel ensemble model using diverse pre-trained CNN architectures and a meta-learner for more accurate KOA classification and grading.

Findings

Achieved 73% accuracy in multiclass classification

Achieved 87.5% accuracy in binary classification

Outperforms previous literature results

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.