Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE credits
In this thesis a novel approach is developed to detect urethral stones based on
a computer-aided process. The input data is a CT scan from the patient, which
is a high-resolution 3D grayscale image. The algorithm developed extracts the
regions that might be stones, based on the intensity values of the pixels in
the CT scan. This process includes a binarizing process of the image, finding
the connected components of the resulting binary image and calculating the
centroid of each of the components selected. The regions that are suspected
to be stones are used as input of a CNN, a modified version of an ANN,
so they can be classified as stone or non-stone. The parameters of the CNN
have been chosen based on an exhaustive hyperparameter search with different
configurations to select the one that gives the best performance. The results
have been satisfactory, obtaining an accuracy of 98,3%, a sensitivity of 99,5%
and a F1 score of 98,3%.
2016. , 71 p.