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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.issued2022en_US
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/SIU55565.2022.9864751en_US
dc.identifier.isbn9781665450928
dc.identifier.urihttps://dx.doi.org/10.1109/SIU55565.2022.9864751
dc.identifier.urihttps://hdl.handle.net/20.500.12511/9829
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.en_US
dc.language.isoengen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.rightsinfo:eu-repo/semantics/embargoedAccessen_US
dc.subjectCNNen_US
dc.subjectDeep Learningen_US
dc.subjectfMRIen_US
dc.subjectImage Processingen_US
dc.subjectSupervised Learningen_US
dc.titlers-fMRI analysis using spatio-temporal sparse convolutional neural networksen_US
dc.typeconferenceObjecten_US
dc.relation.ispartof30th Signal Processing and Communications Applications Conference, SIU 2022en_US
dc.departmentİstanbul Medipol Üniversitesi, Sağlık Bilimleri Enstitüsü, Sinirbilim Ana Bilim Dalıen_US
dc.departmentİstanbul Medipol Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Elektrik ve Elektronik Mühendisliği Bölümüen_US
dc.departmentİstanbul Medipol Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.departmentİstanbul Medipol Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Biyomedikal Mühendisliği Bölümüen_US
dc.authorid0000-0002-9282-6568en_US
dc.authorid0000-0001-7853-1731en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.identifier.doi10.1109/SIU55565.2022.9864751en_US
dc.institutionauthorYıldız, Sultan
dc.institutionauthorHafeez, Muhammad Adeel
dc.institutionauthorKayasandık, Cihan Bilge
dc.institutionauthorDoğan, Merve Yüsra
dc.identifier.scopus2-s2.0-85138683195en_US


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