CC BY 4.0Osman, AhmadVega Arellano, Jaime PaoloJaime PaoloVega Arellano2022-10-072022-10-072022-09https://publica.fraunhofer.de/handle/publica/427174https://doi.org/10.24406/publica-36710.24406/publica-367Stroke is a severe condition that causes a high mortality rate worldwide. Medical image analysis is the main method that specialist employ in hospitals to localize and detect strokes. Despite the on-site software at hospitals and the expertise of neurologists and radiologists, it is still a challenging, time-consuming and labor-intensive task for them. Convolutional Neural Networks is the preferable strategy to analyze images and contribute to the medical diagnoses with a considerable reliability in their results. In fact, Deep Learning techniques have evolved at a rapid peace with new architectures that are currently assisting the medical sector to make such tasks more efficient when it comes to preserve patients’ lives. Despite such enhancements, the complexity of the diseases by nature makes it necessary to continue excelling at this task in an automatic approach. Therefore, the ultimate goal of this thesis is to implement a two-stage architecture to segment brain lesions, and the classify the results to distinguish correct and incorrect cases automatically, with a particular focus on maximizing performance on the segmentation part by training state-of-the-art neural network architectures to detect brain lesions in patients.enMRIStroke detectionDeep LearningImage SegmentationATLASU-NetDDC::600 Technik, Medizin, angewandte Wissenschaftenmaster thesis