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