Soft attention based DenseNet model for Parkinson's disease classification using SPECT images
Document Type
Article
Publication Date
7-13-2022
Abstract
ObjectiveDeep learning algorithms have long been involved in the diagnosis of severe neurological disorders that interfere with patients' everyday tasks, such as Parkinson's disease (PD). The most effective imaging modality for detecting the condition is DaTscan, a variety of single-photon emission computerized tomography (SPECT) imaging method. The goal is to create a convolutional neural network that can specifically identify the region of interest following feature extraction. MethodsThe study comprised a total of 1,390 DaTscan imaging groups with PD and normal classes. The architecture of DenseNet-121 is leveraged with a soft-attention block added before the final classification layer. For visually analyzing the region of interest (ROI) from the images after classification, Soft Attention Maps and feature map representation are used. OutcomesThe model obtains an overall accuracy of 99.2% and AUC-ROC score 99%. A sensitivity of 99.2%, specificity of 99.4% and f1-score of 99.1% is achieved that surpasses all prior research findings. Soft-attention map and feature map representation aid in highlighting the ROI, with a specific attention on the putamen and caudate regions. ConclusionWith the deep learning framework adopted, DaTscan images reveal the putamen and caudate areas of the brain, which aid in the distinguishing of normal and PD cohorts with high accuracy and sensitivity.
Keywords
Neural networks, Parkinson's disease (PD), DenseNet architecture, Region of convergence (ROC), Area under the curve
Divisions
biomedengine
Funders
Universiti Malaya,ACU United Kingdom (Grant No: IF 063-2021)
Publication Title
Frontiers in Aging Neuroscience
Volume
14
Publisher
Frontiers Media SA
Publisher Location
AVENUE DU TRIBUNAL FEDERAL 34, LAUSANNE, CH-1015, SWITZERLAND