*4.3. Sensitivity Analysis of the Number of Training Subjects*

During this experiment, we discuss how the number of subjects used for training can affect the level of accuracy. We repeated the above experiment with different percentages of subjects used for training (between 20% and 80%), the average accuracy of which is reported in Table 6.


**Table 6.** Sensitivity analysis of the proposed architectures against the number of training subjects in terms of the reported testing accuracy (ACC %).

The reported results show that increasing the number of subjects (70% and 80%) during the training improves the testing accuracy. Although this behavior is the same for the three proposed architectures, we can see that the 2D-CNN model consistently outperforms the other two models with an accuracy of 97.10% when using 80% of the subjects in the dataset for training. In contrast, decreasing the number of subjects during the training degrades the testing accuracy. Despite the decrease in testing accuracy, the level achieved is still acceptable (89.71%, 89.31%, and 90.79%) for the FC, 1D-CNN, and 2D-CNN, respectively when using 20% of the subjects for training.

## *4.4. Sensitivity with Respect to the Pre-Trained CNN*

In order to further assess the sensitivity of the proposed approach with respect to the pre-trained CNN model, we carried out additional experiments using other well-known pre-trained CNN models: Inception-v3 [61], DenseNet [62], and residual network (ResNet) [63]. We used these recent pre-trained models for this experiment as they require a comparatively small number of parameters as shown in Table 7.

Inception-v3 is one of Inception models family developed by Google [61], in which they introduced the concept of factorizing of convolutions. Inceptions models are based on increasing the width and depth of the network, by utilizing a module called inception [64], which contains several convolutional layers with different filter sizes. The utilization of the inception module allows the Inception model for a better deal with scale and spatial variations. DenseNet was proposed by Szegedy et al. [62] for better utilization of computing resources. DeneNet is based on adding connections between each layer and every other layer in feed-forward fashion, in which each layer receives the feature maps of all preceding layers as input and feeds its own feature maps as input into all subsequent layers. ResNet was proposed by He et al. [63] to overcome the difficulties in training deeper networks by learning residual functions. These residual networks achieved better optimization and generalization as the depth increases.


**Table 7.** Classification Accuracy using different pre-trained CNN models. We used Inception-v3, DenseNet-169, and ResNet-50.

We note from the results in Table 7, that EfficientNet-B3 achieves the highest accuracies in the three architectures and outperforms the other pre-trained CNNs. The accuracies of EfficientNet-B3 in the three architecture (94.99%, 94.84%, and 95.32%) consistently exceed the best accuracies of the other models except for Inception-v3 which achieves a comparable result in the case of 2D-CNN (95.20%). However, EfficientNet-B3 requires the minimum number of parameters (10 M after removing the top layers), whereas Inception-v3 and resNet-50 require 21 M and 23 M, respectively (after removing the top layers). Finally, from the reported accuracies, we note that 2D-CNN outperforms the FC and 1D-CNN for all the pre-trained models (i.e., 95.32%, 95.20%, 93.29%, and 94.00% for EffieicentNet-B3, Inception-v3, DenseNet-169, and ResNet-50 respectively).
