Comparative Analysis of Performance of Deep Learning Models in Knee Arthritis Classification

Abstract

To keep away from irreversible joint damage and systemic issues, knee septic arthritis, a important joint contamination, desires to be recognized rapid and accurately. Traditional analysis techniques that rely on laboratory consequences and medical assessment can be hard and reason healing delays. In order to pick out and categorize knee septic arthritis, this take a look at indicates an AI-based completely diagnostic framework that makes use of X-ray imaging and deep learning models, which include CNet, GNet, TabNet, ResNet18, DenseNet and EfficientNetV2B0. The era uses modern day image characteristic extraction strategies to boom precision and dependability, giving scientific specialists a useful tool for choice-making. High diagnostic overall performance is confirmed within the experimental assessment, underscoring the usefulness of the advised method for scientific treatment making plans and early identification.

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