Hybrid Feature Fusion Using RNN and Pre-trained CNN for Classification of Alzheimer's Disease

The accurate classification of AD is very essential for both patient and social care, and it will be more significant once the treatment options are available to reverse the progress of the disease. The recent success of deep learning techniques has rapidly advanced the automatic classification of AD using neuroimaging biomarkers such as MRI. However, there exist two major challenges. First, training a deep convolutional neural network (CNN) from scratch relies on a large number of labeled training data to obtain high accuracy without overfitting. Second, due to high computational cost, most of the existing techniques employ 2D CNN that cannot leverage the complete spatial information; hence, it loses the inter-slice correlation. To address these limitations, we combine a recurrent neural network (RNN), specifically long short-term memory (LSTM) on top of the bottleneck layer of pre-trained DenseNet architecture, a deep CNN has already been trained on a large-scale dataset called ImageNet. In addition to the intra-slice features extracted from the deep CNN, the proposed technique exploits the inter-slice features through LSTM in order to discriminate the patients having AD and cognitively normal (CN) clinical status from the brain MRI data. Through experimental results, we show that our proposed model has better performance than state-of-the-art deep learning methods on the Open Access Series of Imaging Studies (OASIS) dataset using 5 -fold cross validation.