rs-fMRI analysis using spatio-temporal sparse convolutional neural networks

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dc.authorid0000-0002-9282-6568
dc.authorid0000-0001-7853-1731
dc.contributor.authorYener, Fatma Muberr
dc.contributor.authorYıldız, Sultan
dc.contributor.authorHafeez, Muhammad Adeel
dc.contributor.authorKayasandık, Cihan Bilge
dc.contributor.authorDoğan, Merve Yüsra
dc.date.accessioned2022-10-12T07:01:11Z
dc.date.available2022-10-12T07:01:11Z
dc.date.issued2022
dc.departmentİstanbul Medipol Üniversitesi, Sağlık Bilimleri Enstitüsü, Sinirbilim Ana Bilim Dalı
dc.departmentİstanbul Medipol Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Elektrik ve Elektronik Mühendisliği Bölümü
dc.departmentİstanbul Medipol Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümü
dc.departmentİstanbul Medipol Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Biyomedikal Mühendisliği Bölümü
dc.description.abstractNeuropsychiatric diseases such as Autism Spectrum Disorder (ASD) and Schizophrenia cause various behavioral and communication dysfunctions in human life. Resting state functional magnetic resonance imaging (rs-fMRI) is used to detect and characterize functional changes in the brain associated with these disorders. Machine learning methods are known to perform well in classifying fMRI images and have proven to have great potential in the field of computer aided diagnosis. In most of the previous studies, hand-crafted features have been used in fMRI analyzes and classifications to date. This prevents the system from being end-to-end and causes spatial or temporal information to be lost due to dimension reduction. The method presented in this study works end-to-end as well as being fed with an entire 4-dimensional fMRI sequence. It is faster than traditional convolutions and recurrent neural networks of the same size, thanks to the sparse convolutional layers that are the building blocks of the network. Experiments with schizophrenia and ASD fMRIs have shown similar performance to those in the literature, despite limited resources.
dc.identifier.citationYener, F. M., Yıldız, S., Hafeez, M. A., Kayasandık, C. B. ve Doğan, M. Y. (2022). rs-fMRI analysis using spatio-temporal sparse convolutional neural networks. 30th Signal Processing and Communications Applications Conference, SIU 2022. Safranbolu, 15-18 May 2022. https://dx.doi.org/10.1109/SIU55565.2022.9864751
dc.identifier.doi10.1109/SIU55565.2022.9864751
dc.identifier.isbn9781665450928
dc.identifier.scopus2-s2.0-85138683195
dc.identifier.scopusqualityN/A
dc.identifier.urihttps://dx.doi.org/10.1109/SIU55565.2022.9864751
dc.identifier.urihttps://hdl.handle.net/20.500.12511/9829
dc.indekslendigikaynakScopus
dc.institutionauthorYıldız, Sultan
dc.institutionauthorHafeez, Muhammad Adeel
dc.institutionauthorKayasandık, Cihan Bilge
dc.institutionauthorDoğan, Merve Yüsra
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relation.ispartof30th Signal Processing and Communications Applications Conference, SIU 2022en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/embargoedAccess
dc.subjectCNN
dc.subjectDeep Learning
dc.subjectfMRI
dc.subjectImage Processing
dc.subjectSupervised Learning
dc.titlers-fMRI analysis using spatio-temporal sparse convolutional neural networks
dc.typeConference Object

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