Automated Trauma Detection by Using Machine Learning

Imaging techniques are widely used for medical diagnostics. This can sometimes lead to a real bottleneck when there is a shortage of medical practitioners, and the images must be manually processed. In such a situation, there is a need to reduce the amount of manual work by automating part of the analysis. In this study, we investigate the potential of a machine-learning algorithm for trauma detection in medical image processing. A new method called ResNet50V2 was developed on the trauma dataset to detect trauma disease. We compare the results of the new method analysis with other state-of-the-art networks. The proposed base model, ResNet50V2, received a score of 99.40%.