  Knee OsteoArthritis (KOA) is a prevalent musculoskeletal disorder that causes
decreased mobility in seniors. The diagnosis provided by physicians is
subjective, however, as it relies on personal experience and the
semi-quantitative Kellgren-Lawrence (KL) scoring system. KOA has been
successfully diagnosed by Computer-Aided Diagnostic (CAD) systems that use deep
learning techniques like Convolutional Neural Networks (CNN). In this paper, we
propose a novel Siamese-based network, and we introduce a new hybrid loss
strategy for the early detection of KOA. The model extends the classical
Siamese network by integrating a collection of Global Average Pooling (GAP)
layers for feature extraction at each level. Then, to improve the
classification performance, a novel training strategy that partitions each
training batch into low-, medium- and high-confidence subsets, and a specific
hybrid loss function are used for each new label attributed to each sample. The
final loss function is then derived by combining the latter loss functions with
optimized weights. Our test results demonstrate that our proposed approach
significantly improves the detection performance.
