Scopus
YÖKSİS Eşleşti
Machine learning based detection of depression from task-based fMRI using weighted-3D-DWT denoising method
Multimedia Tools and Applications · Ocak 2024
YÖKSİS Kayıtları
Machine learning based detection of depression from task-based fMRI using weighted-3D-DWT denoising method
Springer Science and Business Media LLC · 2023 SCI-Expanded
DOKTOR ÖĞRETİM ÜYESİ RUKİYE TEKDEMİR →
Machine learning based detection of depression from task-based fMRI using weighted-3D-DWT denoising method
Multimedia Tools and Applications · 2024 SCI-Expanded
DOKTOR ÖĞRETİM ÜYESİ RUKİYE TEKDEMİR →
Machine learning based detection of depression from task-based fMRI using weighted-3D-DWT denoising method
Multimedia Tools and Applications · 2024 SCI-Expanded
PROFESÖR ÖZKAN GÜLER →
Machine learning based detection of depression from task-based fMRI using weighted-3D-DWT denoising method
Multimedia Tools and Applications · 2024 SCI-Expanded
DOKTOR ÖĞRETİM ÜYESİ GÜZİN ÖZMEN →
Makale Bilgileri
DergiMultimedia Tools and Applications
Yayın TarihiOcak 2024
Cilt / Sayfa83 · 11805-11829
Scopus ID2-s2.0-85163191822
Özet
Depression has become an important public health problem in recent years because the probability of a depressive episode in a person's entire life is generally between 18-20%. Neuroimaging techniques investigate diagnostic biomarkers in depression disorders and support traditional communication-based diagnosis in psychiatry. The quality of the brain images used in functional MRI (fMRI), and the design of decision support systems using these images are essential for accurate diagnosis. The Gaussian smoothing for fMRI preprocessing blurs the image for statistical analysis but is inadequate because image detail is lost during filtering, leading to poor classification results. We argue that the weighted-3 Dimensional-Discrete Wavelet Transform (weighted-3D-DWT) denoising approach instead of Gaussian smoothing for task-based fMRI. The activation maps show differences in intensity values in the cluster size of voxels in the mood-related regions between patients and control subjects (p<0.05). Thus, we classify depression disorders using a machine learning approach and improve the classification accuracy using weighted-3D-DWT. The classification results show that weighted-3D- DWT with Random Forest and 10-fold cross-validation achieves 96.4% accuracy, while Gaussian Smoothing with a Support Vector Machine achieves 83.9% classification accuracy. Classification accuracy increases to 97.3% when 30 components are used with principal component analysis. Our results show that an fMRI experiment with visual stimuli that can aid the diagnosis of depression provides significant classification accuracy using weighted-3D-DWT.
Yazarlar (5)
1
Güzin Özmen
ORCID: 0000-0003-3007-5807
2
Seral Özşen
3
Y. Paksoy
4
Ozkan Guler
5
Rukiye Tekdemir
ORCID: 0000-0001-7912-5727
Anahtar Kelimeler
3D-Discrete wavelet transform
Depression
fMRI
Machine learning
PCA
Spm.T
Kurumlar
Hamad Medical Corporation
Doha Qatar
Konya Technical University
Konya Turkey
Qatar University
Doha Qatar
Selçuk Tip Fakültesi
Konya Turkey
Selçuk Üniversitesi
Selçuklu Turkey
Metrikler
3
Atıf
5
Yazar
6
Anahtar Kelime