Tibial Injury Detection using Convolutional Neural Networks - da Rocha, Matheus Bonfim; Marcato, Bruno Uhlmann; auf der Strasse, Wally; Taniguchi, Maiara Mitiko; Seixas Junior, José Luis; Campos, Daniel Prado; Mantovani, Rafael Gomes (Anais do Symposium on Knowledge Discovery, Mining and Learning (KDMiLe), 2025)
Bone fractures are common traumas in hospital orthopedic departments. Thermal images in an orthopedic emergency setting indicate the exact location of the traumatic injury, facilitating the acquisition of radiological images and the correct patient positioning, avoiding the acquisition of complementary images. Despite significant progress in the area, there is still a need to develop thermal image automated techniques that provide robust, accurate, and detailed classification. Most studies segment manually regions of interest and establish threshold temperature values using specific thermal image processing software. Thus, in this study, we evaluated the use and effectiveness of convolutional neural networks for tibia injury detection with thermographic images. Experiments were performed with a real dataset developed by UTFPR/UFPR universities under the ethical guidelines of Resolution 466/12, with the approval of the Research Ethics Committees of the Federal and Hospital das Clínicas of the Federal University of Paraná (UFPR). The results were promising, showing that VGG19 could accurately recognize healthy and unhealthy patients with an average F-Score of 0.894. Although not statistically accurate like VGG results, traditional ML baselines could unveil some important image features that could explain the decision process, most related to the red channel values, saturation, and image texture.