Faculty, Staff and Student Publications
Publication Date
6-24-2024
Abstract
Parkinson's disease (PD) is challenging for clinicians to accurately diagnose in the early stages. Quantitative measures of brain health can be obtained safely and non-invasively using medical imaging techniques like magnetic resonance imaging (MRI) and single photon emission computed tomography (SPECT). For accurate diagnosis of PD, powerful machine learning and deep learning models as well as the effectiveness of medical imaging tools for assessing neurological health are required. This study proposes four deep learning models with a hybrid model for the early detection of PD. For the simulation study, two standard datasets are chosen. Further to improve the performance of the models, grey wolf optimization (GWO) is used to automatically fine-tune the hyperparameters of the models. The GWO-VGG16, GWO-DenseNet, GWO-DenseNet + LSTM, GWO-InceptionV3 and GWO-VGG16 + InceptionV3 are applied to the T1,T2-weighted and SPECT DaTscan datasets. All the models performed well and obtained near or above 99% accuracy. The highest accuracy of 99.94% and AUC of 99.99% is achieved by the hybrid model (GWO-VGG16 + InceptionV3) for T1,T2-weighted dataset and 100% accuracy and 99.92% AUC is recorded for GWO-VGG16 + InceptionV3 models using SPECT DaTscan dataset.
Keywords
Humans, Parkinson Disease, Deep Learning, Tomography, Emission-Computed, Single-Photon, Algorithms, Magnetic Resonance Imaging, Male, Female, Parkinson’s disease; SPECT DaTscan; T1, T2-weighted; Deep learning; VGG16; InceptionV3; Grey wolf optimization
DOI
10.1186/s12880-024-01335-z
PMID
38910241
PMCID
PMC11194992
PubMedCentral® Posted Date
6-24-2024
PubMedCentral® Full Text Version
Post-print
Published Open-Access
yes