Automatic Diagnosis of Scaphoid Fractures in Wrist Radiographs Using Convolutional Neural Network Architectures

• Luiz F. A. Guerra UNDB
• Carine de O. Vieira UNDB
• Augusto C. R. Xavier UNDB
• Giovanni L. F. da Silva UNDB
• João O. B. Diniz IFMA

DOI: https://doi.org/10.5753/sbcas.2026.21719

Abstract

Scaphoid fractures represent a persistent challenge in wrist radiography, especially in non-displaced cases and in the early stages of trauma. Therefore, this work proposes a method based on Convolutional Neural Networks for the automatic detection and diagnosis of scaphoid fractures in wrist radiographs. The proposed method employs Faster R-CNN with ResNet-50 backbone for scaphoid detection and InceptionResNetV2 for fracture classification. Preliminary results demonstrated good performance with an IoU of 93.1% in scaphoid detection and 98.22% accuracy in fracture diagnosis. The findings indicate strong potential for this approach to support the radiographic diagnosis of scaphoid fractures.

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