  Deep Neural Networks - especially Convolutional Neural Network (ConvNet) has
become the state-of-the-art for image classification, pattern recognition and
various computer vision tasks. ConvNet has a huge potential in medical domain
for analyzing medical data to diagnose diseases in an efficient way. Based on
extracted features by ConvNet model from MRI data, early diagnosis is very
crucial for preventing progress and treating the Alzheimer's disease. Despite
having the ability to deliver great performance, absence of interpretability of
the model's decision can lead to misdiagnosis which can be life threatening. In
this thesis, learned shape features and abstractions by 3D ConvNets for
detecting Alzheimer's disease were investigated using various visualization
techniques. How changes in network structures, used filters sizes and filters
shapes affects the overall performance and learned features of the model were
also inspected. LRP relevance map of different models revealed which parts of
the brain were more relevant for the classification decision. Comparing the
learned filters by Activation Maximization showed how patterns were encoded in
different layers of the network. Finally, transfer learning from a
convolutional autoencoder was implemented to check whether increasing the
number of training samples with patches of input to extract the low-level
features improves learned features and the model performance.
