Abstract
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.
Abstract (translated)
URL
https://arxiv.org/abs/1909.04142
