# Explainable AI for Alzheimer Detection: A Review of Current Methods and Applications
## 2 Techniques in Alzheimer's Disease Detection
### Deep Learning Models
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Author** & **Dataset** & **Data Type** & **Models Used** & \begin{tabular}{c} **Explanation** \\ **(Model- \\ **Agnostic**) \\ \end{tabular} & \begin{tabular}{c} **Explanation** \\ **(Model- Specific)** \\ \end{tabular} & \begin{tabular}{c} **Highest** \\ **Accuracy** \\ \end{tabular} \\ \hline \multirow{4}{*}{[16]} & \multirow{4}{*}{Participants} & \multirow{4}{*}{Facial images} & Xception, & \multirow{4}{*}{-} & \multirow{4}{*}{\begin{tabular}{c} Keras, \\ TensorFlow \\ \end{tabular} } \\  & & & & & \\ \cline{1-1}  & & & & & \\ \cline{1-1}  & & & & & \\ \cline{1-1}  & & & & & & \\ \hline \multirow{2}{*}{[30]} & \multirow{2}{*}{UK Biobank} & \multirow{2}{*}{Fundus image} & CNN & \multirow{2}{*}{LAVA} & \multirow{2}{*}{-} & \multirow{2}{*}{71.4\%} \\ \hline \multirow{2}{*}{[45]} & \multirow{2}{*}{ADNI} & MRI & \begin{tabular}{c} SVM, RF, ETC, \\ XGB, and MLP \\ \end{tabular} & & - & - & 86.57\% \\ \hline \multirow{2}{*}{[13]} & \multirow{2}{*}{ADNI} & MRI & VGG16 & non & non & 98.17\% \\ \hline \multirow{2}{*}{[25]} & \multirow{2}{*}{OASIS} & Clinical data, & \begin{tabular}{c} RF, LR, DT, MLP, \\ KNN, GB, AdaB, \\ SVM, and NB \\ \end{tabular} & SHAP & DT & 98.81\% \\ \hline \multirow{2}{*}{[50]} & \multirow{2}{*}{Participants} & \multirow{2}{*}{DTI} & SVM, logistic & \multirow{2}{*}{non} & \multirow{2}{*}{non} & \multirow{2}{*}{82.35\%} \\  & & & & & \\ \cline{1-1}  & & & & & & \\ \hline \multirow{2}{*}{[24]} & \multirow{2}{*}{ADNI} & \multirow{2}{*}{MRI} & DT, CNNs, and & \multirow{2}{*}{-} & \multirow{2}{*}{DT} & \multirow{2}{*}{91\%} \\  & & & & & \\ \cline{1-1}  & & & & & & \\ \hline \multirow{2}{*}{[26]} & \multirow{2}{*}{ADNI} & \multirow{2}{*}{sMRI} & ResNet-based 3D, & \multirow{2}{*}{Score-CAM} & \multirow{2}{*}{-} & \multirow{2}{*}{89.02\%} \\  & & & & & \\ \cline{1-1}  & & & & & & \\ \hline \multirow{2}{*}{[14]} & Kaggle, OASIS & MRI & KNN, SVM, and & \multirow{2}{*}{SHAP} & \multirow{2}{*}{-} & \multirow{2}{*}{99.9\%} \\  & & & & & & \\ \hline \multirow{2}{*}{[15]} & Kaggle & MRI & CNN & Grad-CAM & - & 93.82\% \\ \hline \multirow{2}{*}{[28]} & Kaggle & MRI & \begin{tabular}{c} Resnet50, VGG16 \\ and Inception v3 \\ \end{tabular} & LIME & - & 86.82\% \\ \hline \multirow{2}{*}{[29]} & \multirow{2}{*}{Kaggle, UNBC} & \multirow{2}{*}{MRI} & DenseNet, & \multirow{2}{*}{Grad-CAM} & \multirow{2}{*}{-} & \multirow{2}{*}{88.4\%} \\  & & & \
\begin{tabular}{c} GoogLeNet, \\ ResNet18, \\ EfficientNet, and \\ RegNet \\ \end{tabular} & Grad-CAM & - & & 88.4\% \\ \hline \multirow{2}{*}{[27]} & OASIS-3 & MRI & CNN & SHAP & - & 89\% \\ \hline \multirow{2}{*}{[10]} & \multirow{2}{*}{ADNI} & \multirow{2}{*}{fMRI} & CNN, DT and a & \multirow{2}{*}{non} & \multirow{2}{*}{DT} & \multirow{2}{*}{98\%} \\  & & & & & \\ \cline{1-1}  & & & & & \\ \cline{1-1}  & & & & & \\ \hline \multirow{2}{*}{[37]} & \multirow{2}{*}{OASIS} & MRI &
\begin{tabular}{c} DT, RF, and \\ AdaBoost \\ \end{tabular} & - & DT & 86.84\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Detailed Comparison of Previous Methods. matter and white matter has been considered insufficient [53]. The results in [54] indicate that improved DL methods significantly outperform other ML techniques in terms of effectiveness, efficiency, and scalability. In [55] each layer of the neural network is decoded, revealing the decision-making processes using DL and then extracting results for each layer of the model. Different transfer learning models were created, namely VGG-19 with 58% training accuracy, MobileNet V2, Inception V3 with 55% training accuracy, ResNet-50 with 45% training accuracy, and a custom model developed by DL algorithms with 62% training accuracy. Each model produced accurately distinct outputs, with MobileNet V2 outperforming the others, with a training accuracy of 67% and a test accuracy of 60%. To provide a deeper understanding of the internal workings of the model, principal component analysis and Grade CAM techniques were used.