Comparative Study of Deep Learning Models for Schizophrenia Classification from fMRI Images

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dc.contributor.advisorHajdu, András
dc.contributor.authorSarkar, Juliet Polok
dc.contributor.departmentDE--Informatikai Kar
dc.date.accessioned2024-06-23T18:46:34Z
dc.date.available2024-06-23T18:46:34Z
dc.date.created2024-04-15
dc.description.abstractThis thesis investigates the effectiveness of five deep-learning architectures for medical image classification (Schizophrenia). The COBRE dataset was used to train and evaluate the models. The outcomes demonstrated notable differences in performance, with high accuracy rates being attained by CNN models. While the CNN-LSTM hybrid model and the enhanced CNN model demonstrated encouraging performance, hardware constraints must be taken into account. The research highlights how crucial it is to choose the right deep-learning architectures for tasks involving the classification of medical images.
dc.description.courseMérnökinformatikus
dc.description.degreeMSc/MA
dc.format.extent58
dc.identifier.urihttps://hdl.handle.net/2437/374630
dc.language.isoen
dc.rights.accessHozzáférhető a 2022 decemberi felsőoktatási törvénymódosítás értelmében.
dc.subjectDeep learning
dc.subjectMedical imaging
dc.subjectConvolutional neural networks (CNNs)
dc.subjectImage classification
dc.subjectfMRI
dc.subjectEDA
dc.subjectNeuroimaging
dc.subject.dspaceInformatics::Information Technology
dc.subject.dspaceInformatics::Computer Science
dc.subject.dspaceBiology::Biotechnology
dc.titleComparative Study of Deep Learning Models for Schizophrenia Classification from fMRI Images
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