Classification of Alzheimer's Disease using fMRI Data and Deep Learning Convolutional Neural Networks

TL;DR

This paper demonstrates that convolutional neural networks, specifically LeNet-5, can effectively classify Alzheimer's disease from fMRI data with high accuracy, aiding early diagnosis and disease staging.

Contribution

The study applies CNNs to fMRI data for Alzheimer's classification, achieving high accuracy and showcasing the potential of deep learning in medical imaging diagnostics.

Findings

Achieved 96.85% accuracy in classifying Alzheimer's from healthy brains.

CNN features are effective in capturing discriminative information in fMRI data.

Method can be extended to predict more complex neurological conditions.

Abstract

Over the past decade, machine learning techniques especially predictive modeling and pattern recognition in biomedical sciences from drug delivery system to medical imaging has become one of the important methods which are assisting researchers to have deeper understanding of entire issue and to solve complex medical problems. Deep learning is power learning machine learning algorithm in classification while extracting high-level features. In this paper, we used convolutional neural network to classify Alzheimer's brain from normal healthy brain. The importance of classifying this kind of medical data is to potentially develop a predict model or system in order to recognize the type disease from normal subjects or to estimate the stage of the disease. Classification of clinical data such as Alzheimer's disease has been always challenging and most problematic part has been always selecting the most discriminative features. Using Convolutional Neural Network (CNN) and the famous architecture LeNet-5, we successfully classified functional MRI data of Alzheimer's subjects from normal controls where the accuracy of test data on trained data reached 96.85%. This experiment suggests us the shift and scale invariant features extracted by CNN followed by deep learning classification is most powerful method to distinguish clinical data from healthy data in fMRI. This approach also enables us to expand our methodology to predict more complicated systems.