Youth Depression Diagnosis Algorithm Based on 3D-WGMobileNet and Transfer Learning

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dc.contributor.authorWang, Y.
dc.contributor.authorGuo, Z. H.
dc.contributor.authorSun, K.
dc.contributor.authorXiao, H. B.
dc.contributor.authorWang, W. M.
dc.coverage.issue1cs
dc.coverage.volume34cs
dc.date.accessioned2025-04-10T12:13:03Z
dc.date.available2025-04-10T12:13:03Z
dc.date.issued2025-04cs
dc.description.abstractDepression is a common mental illness that not only profoundly infests the psychological state of patients, but also tends to cause damage to the functioning of patients' brain areas. To construct a comprehensive and detailed framework for a supporting diagnostic network that will help physicians make accurate and timely diagnoses when dealing with patients at different stages of depression, a network model based on three-dimensional (3D) weight group MobileNet (3D-WGMobileNet) and transfer learningis proposed. Firstly, fMRI data is preprocessed, and regional homogeneity analysis is used to reduce the dimension of the image. Then, the characteristics of Alzheimer's disease are learned by transfer learning and transferred to the proposed model. Next, the dynamic group convolution was used to construct the expert weight matrix of the convolution kernel, and the sliding window group convolution was used to compress the parameters of the model to improve the expression ability and computing power of the model. By using 5-fold cross-validation, we conducted experiments using data from HCP and REST-meta-MDD. The experiment results show that the proposed model gives a superior performance compared with other state-of-the-art methods, especially on the classification of the healthy group with major depression groups, where the two datasets achieve 88% and 91% accuracy, respectively, which verifies the feasibility and effectiveness of our model.en
dc.formattextcs
dc.format.extent18-27cs
dc.format.mimetypeapplication/pdfen
dc.identifier.citationRadioengineering. 2025 vol. 34, iss. 1, s. 18-27. ISSN 1210-2512cs
dc.identifier.doi10.13164/re.2025.0018en
dc.identifier.issn1210-2512
dc.identifier.urihttps://hdl.handle.net/11012/250873
dc.language.isoencs
dc.publisherRadioengineering Societycs
dc.relation.ispartofRadioengineeringcs
dc.relation.urihttps://www.radioeng.cz/fulltexts/2025/25_01_0018_0027.pdfcs
dc.rightsCreative Commons Attribution 4.0 International licenseen
dc.rights.accessopenAccessen
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.subjectDepressionen
dc.subjectfunctional magnetic resonance imagingen
dc.subjecttransfer learningen
dc.subjectMobileNeten
dc.subjectdynamic group convolutionen
dc.titleYouth Depression Diagnosis Algorithm Based on 3D-WGMobileNet and Transfer Learningen
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
eprints.affiliatedInstitution.facultyFakulta elektrotechniky a komunikačních technologiícs
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