An experimental analysis of different Deep Learning based Models for Alzheimer’s Disease classification using Brain Magnetic Resonance Images

Abstract

Classification of Alzheimer’s disease (AD) is one of the most challenging issues for neurologists. Manual methods are time consuming and may not be accurate all the time. Since, brain is the most affected region in AD, a proper classification framework using brain images may provide more accurate results. Deep Learning (DL) is a popular representation of machine learning techniques, that emulate the functionalities of a human brain to process information and creates patterns that help in making complex decisions. The ability to absorb information, even from the unstructured and unlabeled data, makes DL one of the first choices by researchers. In this paper, some of the most popular DL models are discussed along with their implementation results for AD classification. All brain Magnetic Resonance (MR) images are acquired from the online data-set, “Alzheimer’s Disease Neuroimaging Initiative (ADNI)”. From the performance comparison amongst all the discussed models, it is observed that the DenseNet-121 model achieves a convincing result with an average performance rate of 88.78%. But one limitation of the DenseNet model is that it uses lots of convolutional operations that make the model computationally slower than many of the discussed models. Depth-wise convolution is a popular way to make a convolutional operation faster and better. Hence, to improve the execution time, we have proposed replacing the convolution layers in the original DenseNet-121 architecture with depth-wise convolution layers. The new architecture also improved the performance of the model with an average rate of 90.22%.