  In recent days, Convolutional Neural Networks (CNN) have demonstrated
impressive performance in medical image analysis. However, there is a lack of
clear understanding of why and how the Convolutional Neural Network performs so
well for image analysis task. How CNN analyzes an image and discriminates among
samples of different classes are usually considered as non-transparent. As a
result, it becomes difficult to apply CNN based approaches in clinical
procedures and automated disease diagnosis systems. In this paper, we consider
this issue and work on visualizing and understanding the decision of
Convolutional Neural Network for Alzheimer's Disease (AD) Diagnosis. We develop
a 3D deep convolutional neural network for AD diagnosis using brain PET scans
and propose using five visualizations techniques - Sensitivity Analysis
(Backpropagation), Guided Backpropagation, Occlusion, Brain Area Occlusion, and
Layer-wise Relevance Propagation (LRP) to understand the decision of the CNN by
highlighting the relevant areas in the PET data.
