An Automated Diagnosis of Parkinson's Disease from MRI Scans Based on Enhanced Residual Dense Network with Attention Mechanism

Basit Öğe Kaydı
dc.contributor.authorAcikgoz, Hakan
dc.contributor.authorKorkmaz, Deniz
dc.contributor.authorTalan, Tarik
dc.date.accessioned2026-06-19T06:41:15Z
dc.date.available2026-06-19T06:41:15Z
dc.date.issued2025
dc.departmentMalatya Turgut Özal Üniversitesi
dc.description.abstractThe 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.
dc.identifier.doi10.1007/s10278-024-01316-2
dc.identifier.endpage1949
dc.identifier.issn2948-2925
dc.identifier.issn2948-2933
dc.identifier.issue4
dc.identifier.orcid0000-0002-5159-0659
dc.identifier.orcid0000-0002-5371-4520
dc.identifier.orcid0000-0002-6432-7243
dc.identifier.pmid39528881
dc.identifier.scopus2-s2.0-105007829086
dc.identifier.scopusqualityN/A
dc.identifier.startpage1935
dc.identifier.urihttps://doi.org/10.1007/s10278-024-01316-2
dc.identifier.urihttps://hdl.handle.net/20.500.12899/6130
dc.identifier.volume38
dc.identifier.wosWOS:001352517100001
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherSpringer
dc.relation.ispartofJournal of Imaging Informatics in Medicine
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WOS_20260612
dc.subjectDiagnosis Of Parkinson'S Disease
dc.subjectMri Classification
dc.subjectDense Network
dc.subjectResidual Learning
dc.subjectAttention Mechanism
dc.titleAn Automated Diagnosis of Parkinson's Disease from MRI Scans Based on Enhanced Residual Dense Network with Attention Mechanism
dc.typeArticle

Dosyalar

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
PubMed İndeksli Yayınlar Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu