## 4 Experimentation and Results
### Model Selection  
This section evaluates different pre-trained deep convolutional networks [48; 49; 50; 51; 52; 53; 54; 55] on the OASIS dataset, as shown in Table 1. It can be observed that the VGG19 model performed the best with an accuracy of 0.977, whereas NASNetLarge model scored the least with an accuracy of 0.912. MobileNet, DenseNet201, ResNet152, EfficientNetV2S, InceptionV3, and Xception scored an accuracy of 0.922, 0.961, 0.946, 0.952, 0.936, and 0.931, respectively. Furthermore, we improve the accuracy of the models using a cropping technique. The results demonstrate that DenseNet201 has a higher AUC score than VGG19, even though the accuracy of DenseNet201 is much less than that of VGG19. This discrepancy can be attributed to the fact that the AUC score only considers the ranking of the predicted probabilities. In contrast, the accuracy score considers both the ranking and the threshold for classification. In some cases, a model with a higher accuracy score may not have a better ROC curve and, thus, a lower AUC score.  
The cropping technique used extracted slices by finding the biggest contour, i.e., inside of the skull where the hippocampus is located. As shown in Figure 3, the extreme points of the brain were localised, which reduces the computation by exempting the pixels which do not contain the information of the features or unwanted space which may hinder the performance of classification model. The unwanted space also acts as noise for the image. To counter that, we applied threshold or canny edge detection, followed by finding the continuous points along the boundary having the same colour or intensity. The extreme points of the top, left, bottom, and right edges of the images were localised, and the same set of models with same hyperparameters as in without cropping the images were trained. As shown in Table 2, the performance of the eight models increased significantly in terms of accuracy and AUC score. It can be observed that VGG19 achieved the highest accuracy score of 0.982, and the lowest accuracy score was achieved by MobileNet. DenseNet201, ResNet152, EfficientNetV2S, InceptionV3, NASNetLarge, and Xception scored accuracies of 0.974, 0.966, 0.975, 0.952, 0.926, and 0.959, respectively. In terms of the AUC, VGG19 performed the best with a score of 0.986, whereas the lowest AUC score of 0.911 was achieved by NASNetLarge.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Models** & **Accuracy** & **Specificity** & **Sensitivity** & **AUC** \\ \hline MobileNet [48] & 0.922 & 0.927 & 0.913 & 0.920 \\ \hline VGG19 [49] & 0.977 & 0.949 & 0.961 & 0.958 \\ \hline DenseNet201 [50] & 0.961 & 0.965 & 0.958 & 0.962 \\ \hline ResNet152 [51] & 0.946 & 0.942 & 0.911 & 0.937 \\ \hline EfficientNetV2S [52] & 0.952 & 0.966 & 0.943 & 0.959 \\ \hline InceptionV3 [53] & 0.936 & 0.939 & 0.923 & 0.932 \\ \hline NASNetLarge [54] & 0.912 & 0.927 & 0.913 & 0.920 \\ \hline Xception [55] & 0.931 & 0.929 & 0.901 & 0.918 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Performance of transfer learning models.  
Figure 3: Illustration of the proposed cropping technique for the extracting region of interest: (**a**) original image, (**b**) finding the biggest contour, (**c**) finding the extreme points, (**d**) region of interest.