Toward Smarter Orthopedic Care: Classifying Plantar Footprints from RGB Images Using Vision Transformers and CNNs

The anatomical structure of the foot can be assessed by examining the plantar footprint for orthopedic intervention. In fact, there is a relationship between a specific type of foot and multiple musculoskeletal disorders, which are among the main ailments affecting the lower extremities, where its accurate classification is essential for early diagnosis. This work aims to develop a method for accurately classifying the plantar footprint and hindfoot, specifically concerning the sagittal plane. A custom image dataset was created, comprising 603 RGB plantar images that were modified and augmented. Six state-of-the-art models have been trained and evaluated: swin_tiny_patch4_window7_224, convnextv2_tiny, deit3_base_patch16_224, xception41, inception-v4, and efficientnet_b0. Among them, the swin_tiny_patch4_window7_224 model achieved 98.013% accuracy, demonstrating its potential as a reliable and low-cost tool for clinical screening and diagnosis of foot-related conditions.