Thakur, Mahima and Kuresan, Harisudha and Dhanalakshmi, Samiappan and Lai, Khin Wee and Wu, Xiang (2022) Soft attention based DenseNet model for Parkinson's disease classification using SPECT images. Frontiers in Aging Neuroscience, 14. ISSN 1663-4365, DOI https://doi.org/10.3389/fnagi.2022.908143.
Full text not available from this repository.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.
| Item Type: | Article |
|---|---|
| Funders: | Universiti Malaya, ACU United Kingdom (Grant No: IF 063-2021) |
| Uncontrolled Keywords: | Neural networks; Parkinson's disease (PD); DenseNet architecture; Region of convergence (ROC); Area under the curve |
| Subjects: | R Medicine > RC Internal medicine > RC0321 Neuroscience. Biological psychiatry. Neuropsychiatry |
| Divisions: | Faculty of Engineering > Biomedical Engineering Department |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 08 Nov 2023 02:57 |
| Last Modified: | 08 Nov 2023 02:57 |
| URI: | http://eprints.um.edu.my/id/eprint/41614 |
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