Despite facing technical issues (e.g., overfitting, vanishing and exploding gradients), deep neural networks have the potential to capture complex patterns in data. Understanding how depth impacts neural networks performance is vital to the advancement of novel deep learning architectures. By varying hyperparameters on two sets of architectures with different depths, this thesis aims to examine if there are any potential benefits from developing deep networks for segmenting intracranial aneurysms from 3D TOF-MRA scans in the ADAM dataset. / Master of Science / With the technologies we have today, people are constantly generating data. In this pool of information, gaining insight into the data proves to be extremely valuable. Deep learning is one method that allows for automatic pattern recognition by iteratively improving the disparity between its prediction and the ground truth. Complex models can learn complex patterns, and such models introduce challenges. This thesis explores the potential benefits of deep neural networks whether they stand to gain improvement despite the challenges. The models will be trained to segment intracranial aneurysms from volumetric images.
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/108224 |
| Date | 07 February 2022 |
| Creators | Boonaneksap, Surasith |
| Contributors | Electrical and Computer Engineering, Jones, Creed Farris, Jia, Ruoxi, Lourentzou, Ismini |
| Publisher | Virginia Tech |
| Source Sets | Virginia Tech Theses and Dissertation |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Format | ETD, application/pdf |
| Rights | In Copyright, http://rightsstatements.org/vocab/InC/1.0/ |
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