Knee OsteoArthritis (KOA) is a common musculoskeletal disorder, which causes reduced mobility for seniors. Early detection of such a disorder is important to limit its impact on people. Computer-Aided Diagnosis (CAD) systems based on deep learning methods have shown success in KOA diagnosis. Due to the high cost of labelling, the lack of sufficient data in the medical field is a significant challenge for training machine learning models. To improve the generalization capability of deep neural network models and avoid overfitting, data augmentation is essential. However, existing data augmentation techniques such as rotation and gamma correction are not effective at increasing the diversity of the original data. In this paper, we propose a novel approach based on the Vision Transformer (ViT) model with a Selective Shuffled Position Embedding (SSPE) strategy that generates different input sequences fixing and shuffling the position embedding of key and non-key patches, respectively, as a novel method of data augmentation for early detection of KOA (KL-0 vs KL-2). Experimental results demonstrate that our approach is valid as it can significantly improve the model’s classification performance.