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This work focuses on providing a convolutional neural network (CNN) architecture for the classification of 3D MRI head scans. In order to offer a comprehensible recommendation for an architecture, this work has four main objectives:
1. Implementation of two CNN architectures based on transfer learning, for the classification of 3D MRI head scans
2. Optimization of both architectures to provide a fair comparison
3. Comparison of both architectures with relevant criteria for the usage of the CNNs in medical diagnosis
4. Evaluation of the effects of transfer learning on both architectures