SliceNet-AD: slice selection-based convolution neural network model for classification of Alzheimer's disease

Abstract

In recent days, a rapid advancement in imaging technologies has tremendously increased the collection of images in the medical field. These emerging technologies have also led the researchers to focus on computer aided diagnosis (CAD) using efficient machine learning and deep learning techniques. In this paper, we have proposed a framework for binary and multiclass classification of Alzheimer's disease (AD) using three-dimensional structural magnetic resonance images (sMRI) and clinical scores from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset. The collected images are subjected to pre-processing using FMRIB Software Library. After pre-processing, the three dimensional grey matter tissues are obtained as an output from tissue segmentation comprises of many two dimensional slices. But, processing and training all the slices requires a lot of computational time. Therefore our aim is to employ convolutional neural network only on the significant slices and also to report the performance of the model. Experimental results prove that the proposed slice selection classification framework achieves better performance when fused with clinical scores.