dc.contributor.advisor | Pan, Chongle | |
dc.contributor.author | Ly, Sinaro | |
dc.date.accessioned | 2024-01-03T16:43:57Z | |
dc.date.available | 2024-01-03T16:43:57Z | |
dc.date.issued | 2023-12-15 | |
dc.identifier.uri | https://hdl.handle.net/11244/340090 | |
dc.description.abstract | The goal of this research is to make a classification program for 3D images by using a CNN model. The images to classify are kidney images that have 3 different classes: Pelvis, Medulla and Cortex.
To do so, a data preprocessing was needed. The data preprocessing went through two big steps: cropping the 3D images to have smaller image volume and rotating the images to get a data enrichment.
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After that data preprocessing, the next step is to build a model that can achieve a better accuracy than 2D models that were used previously. | en_US |
dc.language | en_US | en_US |
dc.subject | preprocessing | en_US |
dc.subject | images | en_US |
dc.subject | machine learning | en_US |
dc.subject | cross-validation | en_US |
dc.title | Kidney OCT 3D images classification using machine learning | en_US |
dc.contributor.committeeMember | Dimitrios, Diochnos | |
dc.contributor.committeeMember | Cheng, Qi | |
dc.date.manuscript | 2023-12-06 | |
dc.thesis.degree | Master of Science | en_US |
ou.group | Gallogly College of Engineering::School of Computer Science | en_US |