DenseCNN: A Densely Connected CNN Model for Alzheimer's Disease Classification Based on Hippocampus MRI Data

Alzheimer's Disease (AD) is a common type of dementia, affecting human memory, language ability and behavior. Hippocampus is an important biomarker for AD diagnosis. Previous hippocampus-based biomarker analyses mainly focused on volume, texture and shape of the bilateral hippocampus. 3D convolutional neural networks (CNNs) can understand and extract complex morphology features from Magnetic resonance imaging (MRI) and have recently been developed for hippocampus-based AD classification. However, existing CNN models often have highly complex structures and require large amounts of training data. Here we propose an accurate and lightweight Densely Connected 3D convolutional neural network (DenseCNN) for AD classification based on hippocampus segments. DenseCNN was trained on 746 and tested on 187 pairs of hippocampus from Alzheimer's Disease Neuroimaging Initiative (ADNI) databases. DenseCNN has an average accuracy of 0.898, sensitivity of 0.985, specificity of 0.852, and area under curve (A UC) of0.979, which are better than or comparable to state-of-art approaches.