An Automated Diagnosis of Parkinson's Disease from MRI Scans Based on Enhanced Residual Dense Network with Attention Mechanism.
The increasing prevalence of neurodegenerative diseases has recently heightened interest in research on early diagnosis of these diseases. Parkinson's disease (PD), among the most prominent of these conditions, is a neurological disorder causing the loss of nerve cells and significantly affecting mo...
| Published in: | Journal of Imaging Informatics in Medicine Vol. 38; no. 4; pp. 1935 - 1950 |
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| Main Authors: | , , |
| Format: | diagnostic images equations & formulas research tables/charts Journal Article |
| Published: |
Springer Nature
Aug2025
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=187278956&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 187278956 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 29482925 NR3A jtl: Journal of Imaging Informatics in Medicine issn: 29482925 maglogo: N pubinfo: dt: Aug2025 vid: 38 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 187278956 187278956 187278956 10.1007/s10278-024-01316-2 187278956 ppf: 1935 ppct: 15 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: An Automated Diagnosis of Parkinson's Disease from MRI Scans Based on Enhanced Residual Dense Network with Attention Mechanism. aug: au: Acikgoz, Hakan Korkmaz, Deniz Talan, Tarık affil: https://ror.org/022ge7714 Department of Software Engineering, Faculty of Engineering, Architecture and Design, Kahramanmaraş İstiklal University, Kahramanmaraş, Turkey sug: subj: Parkinson Disease Diagnosis Magnetic Resonance Imaging Diagnosis, Computer Assisted Automation Convolutional Neural Networks Evaluation Deep Learning Evaluation Attention Early Diagnosis Human Disease Progression Prevention and Control Technology Precision Sensitivity and Specificity Descriptive Statistics Experimental Studies Image Processing, Computer Assisted Image Enhancement ab: The increasing prevalence of neurodegenerative diseases has recently heightened interest in research on early diagnosis of these diseases. Parkinson's disease (PD), among the most prominent of these conditions, is a neurological disorder causing the loss of nerve cells and significantly affecting movement control. Detection of PD in early stages is of critical importance to prevent the progression of the disease and improve treatment processes. The aim of the current study is to develop a deep learning model that can perform accurate classification for early diagnosis of PD from MRI images. In this study, a densely connected feature fusion network with residual learning is designed to diagnose PD patients. The designed network consists of a serial dense block with skip connections and efficient attention mechanisms. In this architecture, squeeze-excitation (SE) blocks with ResNeXt (SE-ResNeXt block) modules are utilized to extract distinctive and high-level features. In the experiments, a publicly available T2-weighted MRI dataset is used, and an offline augmentation process is applied to limited data to increase the generalization ability and classification performance. The proposed method is evaluated and compared with current state-of-the-art deep learning methods. The obtained results show that the proposed model gives higher classification performance with an overall accuracy of 94.44%, precision of 91.67%, sensitivity of 91.67%, specificity of 95.83%, F1-score of 91.67%, and Matthew's correlation coefficient of 87.50% for the PD and healthy control subjects. pubtype: Academic Journal doctype: diagnostic images equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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