Accurate Detection of Alzheimer's Disease Using Lightweight Deep Learning Model on MRI Data

Abstract

Alzheimer's disease (AD) is a neurodegenerative disorder characterized by cognitive impairment and aberrant protein deposition in the brain. Therefore, the early detection of AD is crucial for the development of effective treatments and interventions, as the disease is more responsive to treatment in its early stages. It is worth mentioning that deep learning techniques have been successfully applied in recent years to a wide range of medical imaging tasks, including the detection of AD. These techniques have the ability to automatically learn and extract features from large datasets, making them well suited for the analysis of complex medical images. In this paper, we propose an improved lightweight deep learning model for the accurate detection of AD from magnetic resonance imaging (MRI) images. Our proposed model achieves high detection performance without the need for deeper layers and eliminates the use of traditional methods such as feature extraction and classification by combining them all into one stage. Furthermore, our proposed method consists of only seven layers, making the system less complex than other previous deep models and less time-consuming to process. We evaluate our proposed model using a publicly available Kaggle dataset, which contains a large number of records in a small dataset size of only 36 Megabytes. Our model achieved an overall accuracy of 99.22% for binary classification and 95.93% for multi-classification tasks, which outperformed other previous models. Our study is the first to combine all methods used in the publicly available Kaggle dataset for AD detection, enabling researchers to work on a dataset with new challenges. Our findings show the effectiveness of our lightweight deep learning framework to achieve high accuracy in the classification of AD.

Keywords: Alzheimer’s disease; Kaggle dataset; MRI data; deep learning; detection; lightweight model.

Figure 1

Sample of the data from Kaggle database [29].

Figure 2

Statistics of Kaggle dataset [29].

Figure 3

Block diagram of all stages of our method.

Figure 4

Model visualization to binary classification task.

Figure 5

Summary of the proposed model for binary classification.

Figure 6

Model visualization to multi-classification task.

Figure 7

Summary of the proposed model for multi-classification.

Figure 8

Summary of the used hyperparameters for our models.

Figure 9

Confusion matrix of the proposed method to detect AD for binary classification tasks.

Figure 10

Loss curves (upper) and accuracy curves (lower) for the training and testing data for the proposed model for binary classification task.

Figure 11

Confusion matrix of the proposed model for multi-classification task.

Figure 12

Loss curves (upper) and accuracy curves (lower) for the training and testing data for the proposed model for multi-classification task.