A New Deep Learning Based End-To Pipeline for Hamstring Injury Detection in Thermal Images of Professional Football Player

Abstract

Football clubs use various methods such as thermal imaging which is a non-invasive and faster method to detect injuries and increase the success rate of the football club by reducing the injury rate. Studies have proven that with thermal imaging it is possible to detect inflammation caused by an injury. Therefore, it is possible to detect potential injury with infrared thermography. One of the biggest handicaps of injury detection with thermal imaging is that it is open to subjective interpretation, there are many points that can be missed, and it takes time to analyse them one by one. In order to avoid this problem, to increase the success of injury detection, a deep learning supported pipeline has been designed in this study to detect injuries from thermal images. In this pipeline, the hamstring muscle region from the football player thermal images was segmented using U-Net architecture. After that in order to detect injuries, segmented muscle region is classified by using Densenet, Resnet, VGG, Efficientnet architectures variations and feature pyramid added at the end of these architectures. Among the architectures used for classification, the EfficientnetB0 and EfficientnetB1+feature pyramid architectures are the most successful, with accuracies of 83.9% and 81%, respectively.