# Predicting Alzheimer's Disease with AI and Brain Imaging Data

Chun-Cheng Peng, Guan-Wei Lin, Jian-Min Lin, Guan-Ting Chen, Wei-Chen Liu

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Chun-Cheng Peng, Guan-Wei Lin, Jian-Min Lin, Guan-Ting Chen, Wei-Chen Liu. Predicting Alzheimer's Disease with AI and Brain Imaging Data. 19th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI), Jun 2023, León, Spain. pp.291-301, 10.1007/978-3-031-34111-3_25. hal-05449793

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# Predicting Alzheimer's Disease with AI and Brain Imaging Data

Chun-Cheng Peng, Guan-Wei Lin(✉), Jian-Min Lin, Guan-Ting Chen, and Wei-Chen Liu

Department of Information and Communication Engineering, Chaoyang University of Technology, Taichung City, Taiwan (ROC)

goudapeng@cyut.edu.tw, {s10830015, s11130623, s10830075, s10830011}@gm.cyut.edu.tw

**Abstract.** According to the latest estimates, one in three elderly people will suffer from dementia in 2050, with the majority of cases being Alzheimer's disease. This study proposes a novel deep-learning CNN architecture for the prediction of Alzheimer's disease, utilizing the OASIS-2 dataset. On average, the proposed approach achieves a testing accuracy of 96.37% for 100-fold cross validation, out-performing related works such as VGG-19 [4], AlexNet [3], and GoogLeNet [3] by 0.55%, 4.97%, and 3.35%, respectively. Experimental results provide positive evidence that the proposed approach, with much fewer parameters, has strong potential to tackle the prediction problem of Alzheimer's disease. In the future, a specialized system can be developed to handle Alzheimer's disease and alleviate the diagnostic burden of doctors. This intelligent system can collaborate with doctors in the early stages of diagnosis, reducing the burden of consultations. If the system achieves higher accuracy, it can enable early detection and treatment, delaying the progression of symptoms. Such a system can detect Alzheimer's disease early, monitor the patient's condition over time, provide personalized treatment plans, and achieve better results.

**Keywords:** Alzheimer's Disease · OASIS · Prediction · Convolutional Neural Networks · Adam Optimizer

## 1 Introduction

Being the most common dementia, Alzheimer's disease (AD) originated in 1906 when the German psychiatrist and pathologist Eros Alzheimer first published senile dementia [1]. More than a hundred years later, as with no radical cure, AD is now believed to be a brain disease that causes problems with memory, thinking, and behavior, where both courses of medication and diseases are possible to be impeded. Under the assumption of early-discovery, early medical treatment and relative healthcare are extremely important to the AD patients and their families. As a result, plenty of AI approaches in smart healthcare were proposed [2–4] in order to tackle this issue.
The progress and development of the medical industry in today's society plays a crucial role in modern people's lives. In the past, many diseases could have irreversible consequences due to a lack of timely and effective diagnosis and treatment. However, today, with the development of technologies such as artificial intelligence [5] and deep learning [6], intelligent medical treatment has become one of the important ways to improve the medical industry.

The development of deep learning technology has brought great help to intelligent medical treatment. Through technologies such as deep learning and machine learning, patients' data can be effectively collected, analyzed, and processed to provide doctors with more accurate diagnosis and treatment plans. In this case, the accuracy and completeness of the data are essential for the training and learning of the artificial intelligence model. Incorrect data will have a negative impact on the model's learning and diagnosis, even leading to incorrect diagnosis and treatment plans.

The application of intelligent medical treatment can not only improve the efficiency and accuracy of the medical industry but also help people better manage their health. For example, in daily life, many people may overlook some abnormal phenomena in their bodies due to busyness or other reasons. However, if intelligent medical treatment technology is used, the patient's data can be used to monitor the body's condition in real-time, and diseases can be detected and treated in a timely manner, thereby better protecting their health.

In summary, the application of intelligent medical treatment is a field worthy of attention and development. Through continuously advancing the development and application of technology, it is believed that in the future, intelligent medical treatment will become more widespread and improved, bringing more benefits to people's health and lives.

This paper adopts deep-learning convolutional neural networks (CNN) [7], and the experimental dataset uses the Open Access Series of Imaging Studies (OASIS). Regarding the application of CNN in medical imaging assistance, current medical image data analysis mostly relies on doctors' naked eye. The observation and reading inspection of medical images, as well as differences based solely on doctor experience, will affect the accuracy of diagnosis to some extent, and the increase in clinical image data has brought a lot of workload to doctors. In recent years, deep learning technology is used to automatically extract features and analyze medical images to provide auxiliary diagnosis, provide doctors with judgment, and reduce the workload of doctors.

## 2 Related Works

Alzheimer's disease is a cognitive impairment that is degenerative and irreversible. It deteriorates over time, leading to a gradual loss of physical function. In recent years, with the development of smart medical care, many scholars have used deep learning to identify Alzheimer's disease, with good results in recognition accuracy.

There are several common methods for diagnosing AD, including medical history, physical examination, psychological testing, MRI and CT scans, and biomarker testing. Among them, fMRI [8] is a new diagnostic method that also has potential in AD diagnosis. Research by Samuel L. Warren et al. [9] has shown that fMRI and deep learning can be used for AD diagnosis. However, due to the complexity and noise issues of fMRI,
further exploration is needed for its clinical applications. To address fMRI noise, Quan Mai et al. [10] used the BrainVGAE neural network for data processing, while Xuan Kan et al. [11] used the FBNetGen neural network to predict clinical outcomes. Tianren Yang et al. [12] conducted related research using fMRI and proposed new architectures and methods that could be used for Alzheimer's disease classification and biomarker research, potentially becoming an important diagnostic tool in the future. The OASIS is a project that aims to make brain neuroimaging datasets freely available to the scientific community. This experiment utilized OASIS-2 [13], which includes MRI scans of elderly individuals aged 60 years and above, with each participant undergoing 2 or more scans separated by at least 1 year. As a result, a total of 373 MRI scans were obtained.

Junxiu Liu et al. [3] proposed a deep separable CNN model for AD classification. In their work, depthwise separable convolution (DSC) was used to replace the traditional network, and the parameter setting and calculation costs were greatly reduced. Figure 1 displays the network architecture proposed in [3]. Experimental results indicate that the DSC algorithm based on OASIS AD detection in this dataset is very successful, and AlexNet and GoogLeNet are also used for migration learning, with an average classification rate of 91.40% and 93.02%. L. Sathish Kumar et al. [2] proposed the use of the OASIS brain dataset for testing. The proposed model used all parts of the human brain, such as axial and shape loss, to detect AD, and applied more than 100,000 MRI images for training, while the final accuracy rate reaches 98.35%. Figure 2 exhibits the methods proposed by [2]. Manimurugan S suggested [4] that early diagnosis is very important; the study utilized a pre-trained deep transfer learning model VGG-19 for MRI images. The main processes are preprocessing, feature extraction, and classification, and 373 MRI images were obtained from the OASIS dataset for evaluation. For 80% training and 20% testing, compared with existing CNN models (such as AlexNet, GoogLeNet, and VGG-16), the evaluation process uses 5 evaluation indicators to test the performance of the model. The final model achieves 96.04% training accuracy and a 95.82% test rate, which is 1.90% to 5.30% higher than the AlexNet and GoogLeNet models in performance. Figure 3 shows the network architecture model proposed by the author. Table 1 summarizes the aforementioned relevant findings.

<figure><img src="image_4.png" /><figcaption>Fig. 1. The author used this model as a reference and basis</figcaption></figure>
<figure><img src="image_5.png" /><figcaption>Fig. 2. Proposed Methodology and Framework</figcaption></figure>

<figure><img src="image_6.png" /><figcaption>Fig. 3. A Detailed Look at the Proposed VGG-19 Architecture</figcaption></figure>

<table><caption>**Table 1.** Synthesis of Related Research Results</caption><thead><tr><td>Paper</td><td>Module</td><td>Dataset</td><td>Accuracy</td></tr></thead><tbody><tr><td>[3]</td><td>Alexnet</td><td>OASIS</td><td>98.35%</td></tr><tr><td>[4]</td><td>Alexnet</td><td>OASIS</td><td>91.40%</td></tr><tr><td>[4]</td><td>GoogLeNet</td><td>OASIS</td><td>93.02%</td></tr><tr><td>[5]</td><td>Vgg-19</td><td>OASIS</td><td>95.82%</td></tr></tbody></table>

## 3 The Proposed Approach

In order to convert medical files into visualized PNG images, this study utilized the powerful NIFTI Image Converter [14], which offers various practical features. For example, with this converter we can easily change the angle of medical files, including 90, 180, and 270 degrees. Before outputting the image, we can also determine the dimension of
the file, whether it is 3D or 4D. To optimize computing resources and further adjust the models and corresponding free parameters, we resize the images to 32 × 32 pixels. The reason behind using such small images is that it reduces the computational complexity of the model and enables faster training, ultimately shortening the time required for model development. In Fig. 4, we present an example of a medical file converted using the NIFTI Image Converter, demonstrating its efficiency and usefulness.

<figure><img src="image_7.png" /><figcaption>Fig. 4. MRI Image with 256 × 256 Pixel Matrix Size</figcaption></figure>

Figure 5 shows the network architecture used in this experiment. The design of this CNN architecture aims to extract features from images through multiple layers of convolution and pooling, and map these features to different classes. Before the convolution operation, the image is transformed into a matrix form, and convolution is performed by setting different convolution kernels. In this CNN architecture, each convolutional layer uses different convolution kernels to detect different features.

After the convolution operation, pooling layers are added to the model to reduce the size of the feature map. Two different pooling methods are used here, namely max pooling and average pooling. Max pooling selects the maximum value in each region, while average pooling calculates the average value of each region. These pooling operations can reduce the size of the feature map while retaining the most important features.

After extracting the features of the image, these features need to be mapped to different classes. To achieve this goal, two fully connected layers are added to the model. In these fully connected layers, each neuron is connected to all neurons in the previous layer, and its output value is adjusted by weight matrices. These fully connected layers can find the best weights and biases through learning to achieve image classification.

To further improve the performance of the model, Rectified Linear Units (ReLU) are added after the convolution, pooling, and fully connected layers. ReLU is a nonlinear function that can convert negative numbers to 0 while retaining positive numbers. This
can effectively reduce the computation and parameter numbers, while improving the accuracy and generalization ability of the model. In addition, since ReLU can effectively avoid the gradient vanishing problem, it has been widely used in deep learning.

<figure><img src="image_8.png" /><figcaption>Fig. 5. Components and Layers of the CNN Model Architecture</figcaption></figure>

Proper optimization algorithms and hyperparameter selection are crucial when training deep learning models, as they can have a significant impact on the model's performance and training speed. In this study, the Adam optimizer was used to optimize the parameters of a CNN model for better training results. The Adam optimizer [15] is an adaptive learning rate optimization algorithm that combines the advantages of the AdaGrad and RMSProp algorithms and dynamically adjusts the learning rate based on the gradient and update history of each parameter. Compared to traditional gradient descent algorithms, the Adam optimizer can converge faster and avoid problems such as vanishing or exploding gradients, making it widely used in the field of deep learning.

In addition to the Adam optimizer, other hyperparameters need to be appropriately set in the experiment, such as learning rate, batch size, and iteration times. The choice of these hyperparameters affects the model's training speed and performance. A larger learning rate can lead to faster training speed, but may also cause the model to oscillate or not converge. A larger batch size can use more samples for training, but it also increases memory overhead and computation time. Iteration times is also an important hyperparameter, as too many iterations can lead to overfitting, while too few iterations can prevent the model from converging. Therefore, choosing the appropriate hyperparameters is crucial, and optimization needs to be performed through experiments and
parameter tuning. In this study, the authors continuously adjusted the values of hyper-parameters and finally obtained the best combination of parameters to maximize the model's accuracy and generalization performance. Table 2 summarizes the modified parameters used in this experiment, including learning rate, batch size, training rounds, and other relevant hyperparameters.

<table><caption>**Table 2.** Adam Optimizer Parameters Used in the Model</caption><thead><tr><td>Parameter name</td><td>Parameter value</td></tr></thead><tbody><tr><td>InitialLearnRate</td><td>0.001</td></tr><tr><td>LearnRateDropFactor</td><td>0.01</td></tr><tr><td>LearnRateDropPeriod</td><td>30</td></tr><tr><td>L2Regularization</td><td>0.0000004</td></tr><tr><td>MaxEpochs</td><td>50</td></tr><tr><td>MiniBatchSize</td><td>128</td></tr><tr><td>Shuffle</td><td>every-epoch</td></tr></tbody></table>

## 4 Experimental Results and Discussions

All simulations of this paper are implemented via the Deep Learning Toolbox, MATLAB 2020a, on a MS-Windows 10 platform equipped with Intel® Core™ i7-2600 CPU @ 3.40 GHZ. For the sake of cross validation, the 50- run and 100-run simulation results are conducted, with the ratio between training, validation and testing 70%, 15% and 15%. Figures 6 and 7 respectively display the simulation curves and corresponding confusion matrices.

It can be observed that, from Table 3, the averaged testing accuracies for our 50- and 100-run cross validations are 96.24% and 96.37%, respectively, which outperform the latest related works, i.e., VGG-19 [4], AlexNet [3] and GoogLeNet [3], by 0.55%, 4.97% and 3.35%, respectively. As a result, our preliminary experimental results provide promising performance for AD prediction, in terms of higher accuracy and low computational complexity.

<table><caption>**Table 3.** Summary of Averaged Simulation Outcomes</caption><thead><tr><td>RUN</td><td>Training (%)</td><td>Validation (%)</td><td>Testing (%)</td></tr></thead><tbody><tr><td>50-RUN</td><td>98.79</td><td>96.29</td><td>96.24</td></tr><tr><td>100-RUN</td><td>99.18</td><td>96.42</td><td>96.37</td></tr></tbody></table>
<figure><img src="image_9.png" /><figcaption>Fig. 6. Learning Curve for a Single Training Run</figcaption></figure>
<figure><img src="image_10.png" /><figcaption>Fig. 7. Model Testing: Confusion Matrices of a Single Run</figcaption></figure>

## 5 Conclusions and Future Works

Being a no-radical-cure disease, AD diagnosis is limited to the proper and precise interpretation of the specific questionnaires and examinations by medical personnel. In this study, a revised CNN-like deep-learning approach for AD prediction is proposed in order to develop medical assistant systems for effective healthcare. Experimental comparison on the OASIS-2 dataset shows that, the proposed approach achieved the averaged training and testing accuracies of 99.18% and 96.37%, under 100-fold cross validation, which is with strong potential to surmount other related deep-learning methods. In the coming future, other OASIS datasets will be taken into consideration in order to further verify the generalization ability and continuously enhance performance of our proposed system.

In the future, we will develop a specialized system for Alzheimer's disease. This intelligent system can collaborate with doctors in the first stage of diagnosing Alzheimer's disease, thus reducing the burden of doctors' consultations. The conducting system will apply intelligent technologies to analyze a patient's vast amount of data, such as blood tests, electroencephalograms, and imaging tests, to provide a comprehensive assessment of the patient's condition.

If the system has a higher accuracy rate, it can also achieve the effect of early detection and early treatment, delaying the deterioration of symptoms. Such a system can detect
Alzheimer's disease early and track a patient's condition over time. The system can also provide a personalized treatment plan for the patient, providing better treatment results. Furthermore, in order to progressively enhance the system performance, approaches of cardinality estimation [16] will be adopted within the data preprocessing stage.

**Acknowledgments.** OASIS-2: Longitudinal: Principal Investigators: D. Marcus, R. Buckner, J. Csernansky, J. Morris; P50 AG05681, P01 AG03991, P01 AG026276, R01 AG021910, P20 MH071616, U24 RR021382.

In addition to the above-mentioned OASIS-2 contributors, hereby, we would like to express our heartfelt gratitude to Bo-Han Liao, Bing-Rong Lee, Jia-Wei Wu, Zhu-Yun Kou, and Wei-Hong Zhong for their valuable help and support in our research. Bo-Han Liao provided valuable opinions, Bing-Rong Lee offered experimental data, Jia-Wei Wu provided technical support, and Zhu-Yun Kou, and Wei-Hong Zhong offered literature resources. Their contributions played an indispensable role in helping us complete this research. We appreciate their generous help, which allowed us to gain more inspiration and understanding during our research, making it easier for us to comprehend the direction and purpose of our study. Without their support and encouragement, our research would not have been completed so smoothly. We will always remember their contributions and appreciate the profound impact and inspiration they have given us for our research.

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