## 3 Results  
### Traditional Machine Learning Techniques  
This section contains the overview of machine learning-based AD detection algorithms, as shown in Table 2. Various algorithms based on support vector machine (SVM) algorithms have been developed over the last 5-6 years. [PERSON] et al. [27] built an SVM-based algorithm \begin{table}
\begin{tabular}{l l l l l l l} \hline
**Ref** & **Method** & \begin{tabular}{l} **Dataset Size** \\ **(\# Images)** \\ \end{tabular} & \begin{tabular}{l} **Sensitivity** \\ **(\%)** \\ \end{tabular} & \begin{tabular}{l} **Specificity** \\ **(\%)** \\ \end{tabular} & \
\begin{tabular}{l} **AUC** \\ **(\%)** \\ \end{tabular} &
\begin{tabular}{l} **F-1** \\ **(\%)** \\ \end{tabular} \\ \hline
[24] & Machine learning & 170 & 54.4 & 83.7 & 72.0 & - \\
[20] & Machine learning & 458 & - & - & 69.0 & 70.0 \\
[19] & Machine learning & 244 & 79.2 & 84.8 & - & 81.5 \\
[25] & Machine learning & 78 & - & - & 74.0 & - \\
[26] & Machine learning & 138 & 82.0 & 86.0 & - & - \\
[27] & Machine learning & 150 & 79.5 & 92.5 & - & - \\
[28] & Machine learning & 77 & - & - & - & - \\
[22] & Deep learning & 15906 & 77.1 & 70.2 & 78.5 & - \\
[29] & Deep learning & 225 & 99.0 & 90.0 & - & 97.0 \\
[30] & Deep learning & 5266 & - & 94.6 & 96.8 & 90.4 \\
[31] & Deep learning & 5751 & 81.6 & - & 90.0 & 82.9 \\
[21] & Deep learning & 12949 & 90.4 & 93.5 & 80.6 & - \\
[23] & Deep learning & 1144 & 80.4 & 86.5 & - & 83.3 \\
[32] & Deep learning & 445 & 83.7 & 89.1 & 92.9 & - \\
[33] & Deep learning & 1136 & - & - & 83.6 & - \\ \hline \end{tabular}
\end{table} 
Table 1: Performance of machine learning- and deep learning-based AD detection methods. to differentiate between healthy, AD, and Parkinson's disease (PD) groups. The dataset comprised volumetric OCT scans acquired from both eyes of 27 healthy, 20 AD, and 28 PD individuals. Texture features were extracted from these images using techniques such as the gray level co-occurrence matrix. An average sensitivity of 79.5% and specificity of 92.5% were attained for AD patients upon k-fold cross-validation evaluation. However, a consideration for this study is that the model has not yet been evaluated on an external test set. Additionally, classification results between PD and AD were not reported, and only the average values were given. The similar approach was taken by [PERSON] et al. [28] to classify transgenic AD mice and wild-type mice from OCT images. An average accuracy of 90% was achieved upon k-fold cross-validation evaluation. The advantage of using mice instead of humans was the possibility of tracking the retina at the initial stages of AD. In another similar study, an SVM model demonstrated 92% accuracy in distinguishing between wild-type and genetically modified AD mice [34]. Moreover, [PERSON] et al. [19] developed an SVM-based model for the identification of AD patients from fundus images. The training and validation set was composed of 244 images acquired from 174 subjects. The blood vessels were segmented to extract the relevant information. Statistical t-tests were conducted for individual pixels to filter out pixels that showed differences between the two groups. Only filtered pixels were used as features for the SVM model. The model attained 84.8% specificity, 79.2% sensitivity, and 81.5% F-1 score based on 5-fold cross-validation. In contrast to other methods using conventional retinal images, [PERSON] et al. [26] built an SVM model based on hyperspectral imaging to classify AD patients. A total of 20 AD and 26 healthy individuals were used in this study. Various features such as the diameter of vessels and textural features as a contrast were calculated and used for the SVM model. The model demonstrated 82% sensitivity and 86% specificity in the 10-fold cross-validation. A challenge of this approach is that making the model widely accessible may be difficult, as hyperspectral imaging is not commonly used. Alongside SVM, other ML-based models have also been proposed.