Detection of Alzheimer's Disease Progression Using Integrated Deep Learning Approaches

Alzheimer's disease (AD) is an intensifying disorder that causes brain cells to degenerate early and destruct. Mild cognitive impairment (MCI) is one of the early signs of AD that interferes with people's regular functioning and daily activities. The proposed work includes a deep learning approach with a multimodal recurrent neural network (RNN) to predict whether MCI leads to Alzheimer's or not. The gated recurrent unit (GRU) RNN classifier is trained using individual and correlated features. Feature vectors are concatenated based on their correlation strength to improve prediction results. The feature vectors generated are given as the input to multiple different classifiers, whose decision function is used to predict the final output, which determines whether MCI progresses onto AD or not. Our findings demonstrated that, compared to individual modalities, which provided an average accuracy of 75%, our prediction model for MCI conversion to AD yielded an improvement in accuracy up to 96% when used with multiple concatenated modalities. Comparing the accuracy of different decision functions, such as Support Vector Machine (SVM), Decision tree, Random Forest, and Ensemble techniques, it was found that that the Ensemble approach provided the highest accuracy (96%) and Decision tree provided the lowest accuracy (86%).

Automated summary

Alzheimer's disease is a degenerative disorder that affects brain cells and causes early deterioration. Mild cognitive impairment is an early sign of Alzheimer's that hinders regular functioning and daily activities. The proposed work utilizes a deep learning approach with a multimodal recurrent neural network to predict whether mild cognitive impairment progresses to Alzheimer's disease. The results show that the prediction model, which combines multiple modalities, achieves an accuracy improvement of up to 96% compared to individual modalities.