DeciViT-Knee 2025 as Precision Fuzzy Techniques of Decision Trees and VIT in Analysis of Knee Osteoarthritis

This article introduces the DeciViT-Knee 2025 protocol, a hybrid deep-learning workflow that combines Vision Transformers (ViT-B/16), Bayesian Decision Trees, and a third Fuzzy Inference Layer for the early and interpretable detection of Knee Osteoarthritis (KOA). The process starts by setting up a GPU-enabled environment in PyTorch 2.x and Hugging Face Transformers. Then, it gets radiographic and MRI datasets from the Osteoarthritis Initiative (OAI) and the Multicenter Osteoarthritis Study (MOST). Before the fine-tuned ViT-B/16 model processes the data to obtain 768-dimensional image embeddings, it is curated through contrast enhancement, artefact removal, and normalization. We use Bayesian Decision Trees to analyze these embeddings and provide us with calibrated probabilistic classifications. The fuzzy-inference layer incorporates linguistic reasoning to express diagnostic uncertainty in terms such as low, medium, or high risk. The model's performance surpasses that of the CNN, ResNet-50, and ViT-only baselines, achieving an accuracy of 92.4%, an AUC of 0.964, an F1 score of 0.891, and a Brier score of 0.088. The protocol demonstrates how combining transformer-based global feature extraction, Bayesian probabilistic reasoning, and fuzzy interpretability can create a clearer, reproducible, and clinician-friendly framework for diagnosing musculoskeletal imaging.