Adaptive Neuro-Fuzzy Inference System Framework for Paediatric Wrist Injury Classification

An Adaptive Neuro-Fuzzy Inference System (ANFIS) framework for paediatric wrist injury classification (fracture versus sprain) was developed utilising infrared thermography (IRT). ANFIS combines artificial neural network (ANN) learning with interpretable fuzzy rules, mitigating the “black-box” limitation of conventional ANNs through explicit membership functions and Takagi-Sugeno rule consequents. Fourteen children (19 fractures, 21 sprains, confirmed by x-ray radiograph) provided thermal image sequences from which three statistically discriminative temperature distribution features namely standard deviation, inter-quartile range (IQR) and kurtosis were selected. A five-layer Sugeno ANFIS with Gaussian membership functions were trained using a hybrid least-squares/gradient descent optimisation and evaluated under three premise-parameter initialisation strategies: random seeding, K-means clustering, and fuzzy C-means (FCM) data partitioning. Five-fold cross-validation guided the selection of membership functions standard deviation (σ) and rule count, yielding an optimal nine-rule model (σ = 0.1). Comparative experiments show K-means initialisation achieved the best balance between convergence speed (about 48 s for 4000 epochs) and generalisation (validation MSE=0.105; AUC=0.87) versus slower but highly precise random initialisation (AUC=1.00) and rapidly convergent yet unstable FCM (AUC=0.66). The proposed K-means–driven ANFIS offered data-efficient decision support, highlighting the potential of thermal feature fusion with neuro-fuzzy modelling to reduce unnecessary radiographs in emergency bone fracture triage.