Kidney OCT 3D images classification using machine learning

dc.contributor.advisor	Pan, Chongle
dc.contributor.author	Ly, Sinaro
dc.contributor.committeeMember	Dimitrios, Diochnos
dc.contributor.committeeMember	Cheng, Qi
dc.date.accessioned	2024-01-03T16:43:57Z
dc.date.available	2024-01-03T16:43:57Z
dc.date.issued	2023-12-15
dc.date.manuscript	2023-12-06
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. \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.identifier.uri	https://hdl.handle.net/11244/340090
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.thesis.degree	Master of Science	en_US
dc.title	Kidney OCT 3D images classification using machine learning	en_US
ou.group	Gallogly College of Engineering::School of Computer Science	en_US