Abstract

Plantar morphology analysis is essential for understanding foot biomechanics and detecting alterations that may affect the musculoskeletal system. This study proposes a method based on infrared thermography and artificial intelligence for the detection of normal and cavus foot types. The proposed approach integrates an automatic segmentation stage to identify the plantar region in thermographic images, followed by a classification stage based on machine learning algorithms and morphological features extracted from the segmented region. For the segmentation stage, a convolutional neural network-based method was developed, achieving an Intersection over Union (IoU) of 83.07% during testing, indicating high agreement between the predicted segmentations and the reference masks. In the classification stage, three machine learning models were evaluated: logistic regression, support vector machine, and k-nearest neighbours. Among them, the k-nearest neighbours model achieved the best performance, reaching an accuracy of 75%, a precision of 80%, an F1-score of 72.2%, and a recall of 66.6%. Overall, the results highlight the potential of combining infrared thermography with machine learning techniques to identify relevant patterns associated with plantar morphology, enabling the automatic detection of normal and cavus foot types.

IPC Classification

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