Deep learning has been used successfully in supervised classification tasks in order to learn complex patterns. The purpose of the study is to apply this machine learning technique to classifying images of brains with different types of tumors: meningioma, glioma, and pituitary. The image dataset contains 233 patients with a total of 3064 brain images with either meningioma, glioma, or pituitary tumors. The images are T1-weighted contrast enhanced MRI (CE-MRI) images of either axial (transverse plane), coronal (frontal plane), or sagittal (lateral plane) planes. This research focuses on the axial images, and expands upon this dataset with the addition of axial images of brains without tumors in order to increase the number of images provided to the neural network. Training neural networks over this data has proven to be accurate in its classifications an average five-fold cross validation of 91.43%.
| Identifer | oai:union.ndltd.org:VANDERBILT/oai:VANDERBILTETD:etd-04112016-224926 |
| Date | 13 April 2016 |
| Creators | Paul, Justin Stuart |
| Contributors | Daniel Fabbri, Bennett Landman |
| Publisher | VANDERBILT |
| Source Sets | Vanderbilt University Theses |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | http://etd.library.vanderbilt.edu/available/etd-04112016-224926/ |
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