Overview / Usage

Alzheimer’s Disease is an incurable, progressive neurological brain disorder. Early diagnosis of Alzheimer’s Disease can help with proper treatment and prevent brain tissue damage.Detection of Alzheimer’s Disease is exacting due to the similarity in Alzheimer’s Disease Magnetic Resonance Imaging (MRI) data and standard healthy MRI data of older people. Recently, advanced deep learning techniques have successfully demonstrated human-level performance in numerous fields including medical image analysis. We propose a deep convolutional neural network for Alzheimer’s Disease diagnosis using brain MRI data analysis.

Methodology / Approach

Our proposed model is a deep convolutional neural network. The model has several layers performing four basic operations - convolution, batch normalization,rectified linear unit, and pooling. The layers in the model follow a particular connection pattern known as dense connectivity, where each layer is connected to every other layer. For final classification, there is a softmax layer with three different output classes: nondemented, mild and moderate AD. For each MRI data, we created patches from three physical planes of imaging: Axial or horizontal plane, Coronal or frontal plane, and Sagittal or median plane. These patches are fed to the proposed network as input.