A multi-stream convolutional neural network for classification of progressive MCI in Alzheimer's disease using structural MRI images

TL;DR

This paper introduces a multi-stream CNN that uses patch-based MRI data and transfer learning to improve early classification of progressive MCI in Alzheimer's disease, achieving high accuracy on the ADNI-1 dataset.

Contribution

It presents a novel multi-stream CNN architecture with transfer learning for classifying stable versus progressive MCI using structural MRI images.

Findings

Achieved an F1-score of 85.96% on ADNI-1 dataset.

Outperformed existing methods for MCI classification.

Utilized anatomical landmark-based patch extraction and transfer learning.

Abstract

Early diagnosis of Alzheimer's disease and its prodromal stage, also known as mild cognitive impairment (MCI), is critical since some patients with progressive MCI will develop the disease. We propose a multi-stream deep convolutional neural network fed with patch-based imaging data to classify stable MCI and progressive MCI. First, we compare MRI images of Alzheimer's disease with cognitively normal subjects to identify distinct anatomical landmarks using a multivariate statistical test. These landmarks are then used to extract patches that are fed into the proposed multi-stream convolutional neural network to classify MRI images. Next, we train the architecture in a separate scenario using samples from Alzheimer's disease images, which are anatomically similar to the progressive MCI ones and cognitively normal images to compensate for the lack of progressive MCI training data. Finally, we transfer the trained model weights to the proposed architecture in order to fine-tune the model using progressive MCI and stable MCI data. Experimental results on the ADNI-1 dataset indicate that our method outperforms existing methods for MCI classification, with an F1-score of 85.96%.