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Knee OsteoArthritis (KOA) is a prevalent musculoskeletal disorder that causesdecreased mobility in seniors. The diagnosis provided by physicians issubjective, however, as it relies on personal experience and thesemi-quantitative Kellgren-Lawrence (KL) scoring system. KOA has beensuccessfully diagnosed by Computer-Aided Diagnostic (CAD) systems that use deeplearning techniques like Convolutional Neural Networks (CNN). In this paper, wepropose a novel Siamese-based network, and we introduce a new hybrid lossstrategy for the early detection of KOA. The model extends the classicalSiamese network by integrating a collection of Global Average Pooling (GAP)layers for feature extraction at each level. Then, to improve theclassification performance, a novel training strategy that partitions eachtraining batch into low-, medium- and high-confidence subsets, and a specifichybrid loss function are used for each new label attributed to each sample. Thefinal loss function is then derived by combining the latter loss functions withoptimized weights. Our test results demonstrate that our proposed approachsignificantly improves the detection performance.

A Confident Labelling Strategy Based on Deep Learning for Improving Early Detection of Knee OsteoArthritis