## 3 Materials and Methods
### Proposed Methodology
#### 3.2.3 Classification  
The classification stage aims to label images as normal or glaucoma based on the results of the previous stage. Table 4 outlines the data used in the classification stage. The segmented images are subjected to augmentation procedures such as brightness modification, arbitrary rotation, and random horizontal flips. These methods are implemented to increase the size of the dataset and improve the ability of the model to generalize to new or unseen data. Table 5 illustrates the augmentation methods and the total count of training images post-augmentation for the ORIGA, RIM-ONE DL, and HRF datasets.  
Training a CNN from scratch requires a large, labeled dataset and powerful GPU hardware, but with limited data, this is impractical. As a solution, we adopt the transfer learning technique, as used by [PERSON] et al., to overcome this limitation [21]. This study aims to classify retinal fundus images for glaucoma disease using a classification network, examining the adoption of a pretrained model based on recent research findings.  
This study utilized the pretrained EfficientNetB0 model to enhance accuracy in glaucoma classification, reducing computational time and exploring alternative models like DenseNet121, VGG19, and ResNet50. The images were resized to 224 \(\times\) 224 pixels to meet the input size of the pretrained models and normalized by dividing pixel values by 255. The proposed model architecture utilizes the pretrained EfficientNetB0 model on the ImageNet dataset, acting as the convolutional base for extracting features from input images, as shown in Figure 3[25]. EfficientNetB0 was chosen for classification due to its efficient model architecture that balances performance and computational cost, making it suitable for accurate glaucoma classification with limited data. Among the models tested, EfficientNetB0 excelled in detecting glaucoma.  
We excluded the final fully connected layer responsible for classification by setting the \"include top\" parameter to False. The model includes a global average pooling 2D layer, a dropout layer with a rate of 0.3 to prevent overfitting, and a fully connected layer with a single unit and a sigmoid activation function, as shown in Figure 4.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multicolumn{3}{c}{**Number of Images**} & \multicolumn{2}{c}{**Balanced Dataset**} & \multirow{2}{*}{**Total**} \\ \cline{2-2} \cline{4-5}  & **Glaucoma** & & & **Normal** & **Glaucoma** & **Normal** \\ \hline ORIGA [16] & 168 & 482 & 168 & 168 & 336 \\ HRF [17] & 15 & 15 & 15 & 15 & 30 \\ RIM-ONE DL [18] & 172 & 313 & 172 & 172 & 344 \\ \hline \hline \end{tabular}
\end{table}
Table 4: The data utilized in the classification phase.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Stage**} & \multirow{2}{*}{**Dataset**} & **Augmentation** & \multicolumn{2}{c}{**Total Count of Images after**} \\  & & **Technique** & **Augmentation** & \\ \hline \multirow{3}{*}{Classification} & ORIGA [16] & RR, & 1006 & 1006 \\  & HRF [17] & BA, & 87 & 87 \\ \cline{1-1}  & RIM-ONE DL [18] & RHF & 959 & 959 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Augmentation methods and post-augmentation training image counts for ORIGA, RIM-ONE DL, and HRF datasets.  
The model was trained using the Adam optimizer with a learning rate of 0.0001 and binary cross-entropy loss function for binary classification. The (train_convolution_base) parameter determined if the EfficientNetB0 base model weights were updated during training, or frozen. Alternative models used pretrained models as the base, with added layers of batch normalization, dropout, and dense layers. Batch normalization was used for regularization and reducing overfitting, while the dropout layer randomly excluded neurons during training to prevent overfitting.