1 research outputs found
DaTscan SPECT Image Classification for Parkinson's Disease
Parkinson's Disease (PD) is a neurodegenerative disease that currently does
not have a cure. In order to facilitate disease management and reduce the speed
of symptom progression, early diagnosis is essential. The current clinical,
diagnostic approach is to have radiologists perform human visual analysis of
the degeneration of dopaminergic neurons in the substantia nigra region of the
brain. Clinically, dopamine levels are monitored through observing dopamine
transporter (DaT) activity. One method of DaT activity analysis is performed
with the injection of an Iodine-123 fluoropropyl (123I-FP-CIT) tracer combined
with single photon emission computerized tomography (SPECT) imaging. The tracer
illustrates the region of interest in the resulting DaTscan SPECT images. Human
visual analysis is slow and vulnerable to subjectivity between radiologists, so
the goal was to develop an introductory implementation of a deep convolutional
neural network that can objectively and accurately classify DaTscan SPECT
images as Parkinson's Disease or normal. This study illustrates the approach of
using a deep convolutional neural network and evaluates its performance on
DaTscan SPECT image classification. The data used in this study was obtained
through a database provided by the Parkinson's Progression Markers Initiative
(PPMI). The deep neural network in this study utilizes the InceptionV3
architecture, 1st runner up in the 2015 ImageNet Large Scale Visual Recognition
Competition (ILSVRC), as a base model. A custom, binary classifier block was
added on top of this base. In order to account for the small dataset size, a
ten fold cross validation was implemented to evaluate the model's performance