Transfer learning based analysis of neurological disorders

This master's thesis represents a study that aims to provide more precise diagnosis and analysis of neurological disorders. For this purpose, deep learning models will be developed and optimized with transfer learning methods, using data obtained by brain imaging methods such as Magnetic Resonance Imaging (MRI) and functional Magnetic Resonance Imaging (fMRI). This approach aims to capture the salient features of neurological disorders more effectively, thereby increasing diagnostic performance. At the same time, this work has the potential to achieve higher precision in the classification of different neurological disorders. While this thesis can make a significant progress in the diagnosis and management of neurological disorders, it can also contribute to the further development of analyzes in this field by increasing the knowledge on how to effectively process neurological imaging data with deep learning models.