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dc.contributor.advisorPan, Chongle
dc.contributor.authorLy, Sinaro
dc.date.accessioned2024-01-03T16:43:57Z
dc.date.available2024-01-03T16:43:57Z
dc.date.issued2023-12-15
dc.identifier.urihttps://hdl.handle.net/11244/340090
dc.description.abstractThe 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. \bigskip 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.languageen_USen_US
dc.subjectpreprocessingen_US
dc.subjectimagesen_US
dc.subjectmachine learningen_US
dc.subjectcross-validationen_US
dc.titleKidney OCT 3D images classification using machine learningen_US
dc.contributor.committeeMemberDimitrios, Diochnos
dc.contributor.committeeMemberCheng, Qi
dc.date.manuscript2023-12-06
dc.thesis.degreeMaster of Scienceen_US
ou.groupGallogly College of Engineering::School of Computer Scienceen_US


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