Computer-Aided Diagnosis of Alzheimer's Disease Using Multiple Features with Artificial Neural Network

Alzheimer's disease (AD) is a progressively neuro-degenerative disorder. In the AD-related research, the volumetric analysis of hippocampus is the most extensive study. However, the segmentation and identification of the hippocampus are highly complicated and time-consuming. Therefore, a MRI-based classification framework is proposed to differentiate between AD's patients and normal individuals. First, volumetric features and shape features were extracted from MRI data. Afterward, Principle component analysis (PCA) was utilized to decrease the dimensions of feature space. Finally, a Back-propagation artificial neural network (ANN) classifier was trained for AD classification. With the proposed framework, the classification accuracy is reached to 88.27% by only using volumetric features and shape features. And, the result achieved up to 92.17% by using volumetric features and shape features with the PCA.