Deep Learning Approach for Early Detection of Alzheimer's Disease

Abstract

Alzheimer's disease (AD) is a chronic, irreversible brain disorder, no effective cure for it till now. However, available medicines can delay its progress. Therefore, the early detection of AD plays a crucial role in preventing and controlling its progression. The main objective is to design an end-to-end framework for early detection of Alzheimer's disease and medical image classification for various AD stages. A deep learning approach, specifically convolutional neural networks (CNN), is used in this work. Four stages of the AD spectrum are multi-classified. Furthermore, separate binary medical image classifications are implemented between each two-pair class of AD stages. Two methods are used to classify the medical images and detect AD. The first method uses simple CNN architectures that deal with 2D and 3D structural brain scans from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset based on 2D and 3D convolution. The second method applies the transfer learning principle to take advantage of the pre-trained models for medical image classifications, such as the VGG19 model. Due to the COVID-19 pandemic, it is difficult for people to go to hospitals periodically to avoid gatherings and infections. As a result, Alzheimer's checking web application is proposed using the final qualified proposed architectures. It helps doctors and patients to check AD remotely. It also determines the AD stage of the patient based on the AD spectrum and advises the patient according to its AD stage. Nine performance metrics are used in the evaluation and the comparison between the two methods. The experimental results prove that the CNN architectures for the first method have the following characteristics: suitable simple structures that reduce computational complexity, memory requirements, overfitting, and provide manageable time. Besides, they achieve very promising accuracies, 93.61% and 95.17% for 2D and 3D multi-class AD stage classifications. The VGG19 pre-trained model is fine-tuned and achieved an accuracy of 97% for multi-class AD stage classifications.

Keywords: Alzheimer’s disease; Brain MRI; Convolutional neural network (CNN); Deep learning; Medical image classification.

Fig. 1

A proportion of people affected by AD according to ages in the United States [5]

Fig. 2

Slices of MR images: Accelerated Sagittal MPRAGE view, Axial Field Mapping view, and 3 Plane. Localizer view from left to right of AD patient

Fig. 3

The proposed framework E²AD²C architecture

Fig. 4

Example of the normalization methods applied on MRI image

Fig. 5

Illustration of the convolutional operation

Fig. 6

The difference among the sigmoid, Relu, and LRelu activation functions [24]

Fig. 7

The 2D-M²IC model architecture

Fig. 8

The 3D-M²IC model architecture

Fig. 9

The comparison of the proposed models with other models for multi-class medical image classification

Fig. 10

The comparison among the proposed models (2D-M²IC, 3D-M²IC, 2D-BMIC, 3D-BMIC, and fine-tuned VGG19 model) with one another

Fig. 11

Training and validation accuracy and loss for 2D-M2IC

Fig. 12

Training and validation accuracy and loss for 3D-M2IC

Fig. 13

The ROC-AUC of the proposed 2D-M2IC

Fig. 14

The ROC-AUC of the proposed 3D-M2IC

Fig. 15

The AD stage prediction for MRI medical images