Application of an Image Segmentation Method for Intracerebral Hemorrhage Images

Abstract

The main objective of this Bachelor's Thesis (TFG) is to develop an algorithm for the segmentation of cerebral hemorrhages, with a focus on facilitating subsequent decision-making in treatment by medical professionals. The algorithm will be based on a convolutional neural network (CNN) architecture, a deep learning technique that has shown great success in image analysis tasks. By employing a CNN-based algorithm for hemorrhage segmentation, the research aims to achieve accurate and reliable results. This will contribute to improving the speed and precision of diagnosis, treatment planning, and patient care in cases of intracerebral hemorrhages.