## 4 Results  
The python libraries such as TensorFlow [71] and Scikit-learn [72] are used to implement the models in this study. NVidia Titan V GPU is used for DL purposes. VGG16, MobileNetV2, InceptionV3, and DenseNet121 are the four CNN models employed in this work with transfer learning. With a batch size of 32, the total epochs were fixed to 30. In medical image classification, recall measurement is usually regarded as a better performance indicator than accuracy, precision, or specificity because it more accurately depicts the clinical value of classification outcomes [73]. The top, dense, fully connected layers are removed in charge of classifying data to build the final classification layer according to the proposed method. Sigmoid activation is used for binary classification. To solve the overfitting problem, the EarlyStopping and ReduceLRonPlateau methods are utilized in this study as regularisation approaches. Depending on the supplied settings, the model will terminate training once it obtains the best outcome before it starts to overfit. EarlyStopping parameter examines the validation loss, and if it is stable or increasing for five epochs, then the model stops iterating and saves the best model. Similarly, if the loss value stays the same for two successive epochs, the ReduceLRonPlateau method modifies the learning rate by a factor of 0.1. Table 3 depicts the parameters employed in the study. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Batch size & 32 \\ Epochs & 30 \\ learning rate & 0.001 \\ Optimizer & Adam \\ Activation & Sigmoid \\ Loss & binary\_crossentropy \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Parameters utilized in this study. Table 4 displays the APTOS's performance with four models. The table shows that the MobileNetV2 and DenseNet121 models have identical recall, precision, and accuracy scores of 0.9850, indicating that they perform equally well on this task. The InceptionV3 model also has a better recall value of 0.9830 but a precision of 0.9683, suggesting a higher true positive rate. The VGG16 model has a much lower recall score of 0.3902 but a high precision score of 0.9952, indicating that it misses more true positive cases. The specificity value of all models is also high, meaning the model correctly identifies negative cases and has fewer false positives. Overall, the APTOS model performs very well, with all models achieving high accuracy scores of at least 0.6981. However, the DenseNet121 and MobileNetV2 models perform best on this task. The following dataset, EyePACS, underwent the same process, and the results are illustrated in Table 5. The DenseNet121 model produced the best results with a minor loss and the highest accuracy. During 30 epochs with 64 batch sizes, DenseNet121 achieved scores of 0.1412 in training loss, 0.2933 in validation loss, and 0.8910 in accuracy, recall, precision, and specificity. The MobileNetV2 and InceptionV3 models also have relatively high recall, precision, and accuracy scores, with recall and precision scores of over 0.87 and accuracy scores of above 0.8773. The VGG16 model, on the other hand, has a lower recall score of 0.7154 but a high precision score of 0.9420, indicating that it accurately identifies true positive cases but misses many cases. The accuracy score for the VGG16 model is 0.8313, which is lower than the other models evaluated. The specificity value of all models is also high, meaning the model correctly identifies negative cases and has fewer false positives. Overall, the EyePACS model performs well, with all models achieving high accuracy scores of at least 0.8313. However, the DenseNet121 model is the best performer on this task. Looking at Table 6, the DenseNet121 model has the highest recall, precision, and accuracy scores, all of which are 0.7582. The MobileNetV2 model also has relatively high recall, precision, and accuracy scores, with 0.75.