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Knee-Joint Osteoarthritis (KOA) is a prevalent cause of global disability andis inherently complex to diagnose due to its subtle radiographic markers andindividualized progression. One promising classification avenue involvesapplying deep learning methods; however, these techniques demand extensive,diversified datasets, which pose substantial challenges due to medical datacollection restrictions. Existing practices typically resort to smallerdatasets and transfer learning. However, this approach often inheritsunnecessary pre-learned features that can clutter the classifier's vectorspace, potentially hampering performance. This study proposes a novel paradigmfor improving post-training specialized classifiers by introducing adaptivevariance thresholding (AVT) followed by Neural Architecture Search (NAS). Thisapproach led to two key outcomes: an increase in the initial accuracy of thepre-trained KOA models and a 60-fold reduction in the NAS input vector space,thus facilitating faster inference speed and a more efficient hyperparametersearch. We also applied this approach to an external model trained for KOAclassification. Despite its initial performance, the application of ourmethodology improved its average accuracy, making it one of the top three KOAclassification models.

Adaptive Variance Thresholding: A Novel Approach to Improve Existing  Deep Transfer Vision Models and Advance Automatic Knee-Joint Osteoarthritis  Classification