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Computer aided renal calculi detection using Convolutional Neural Networks
Örebro University, School of Science and Technology, Örebro University, Sweden.
2016 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

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%.

Place, publisher, year, edition, pages
2016. , 71 p.
National Category
Computer Science
Identifiers
URN: urn:nbn:se:oru:diva-52254OAI: oai:DiVA.org:oru-52254DiVA: diva2:971220
Subject / course
Computer Engineering
Supervisors
Examiners
Available from: 2016-09-15 Created: 2016-09-15 Last updated: 2016-09-15Bibliographically approved

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School of Science and Technology, Örebro University, Sweden
Computer Science

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