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Imaging techniques is widely used for medical diagnostics. This leads in somecases to a real bottleneck when there is a lack of medical practitioners andthe images have to be manually processed. In such a situation there is a needto reduce the amount of manual work by automating part of the analysis. In thisarticle, we investigate the potential of a machine learning algorithm formedical image processing by computing a topological invariant classifier.First, we select retrospectively from our database of postmortem computedtomography images of rib fractures. The images are prepared by applying a ribunfolding tool that flattens the rib cage to form a two-dimensional projection.We compare the results of our analysis with two independent convolutionalneural network models. In the case of the neural network model, we obtain an$F_1$ Score of 0.73. To access the performance of our classifier, we computethe relative proportion of images that were not shared between the two classes.We obtain a precision of 0.60 for the images with rib fractures.

Automated Rib Fracture Detection of Postmortem Computed Tomography Images Using Machine Learning Techniques