## 4 Results
### CNN Results  
In this section, the performance results of 14 different CNN models tested for HR classification are presented and analyzed. The classification performance of CNN models is provided in Table 7. DenseNet169 was the most successful model with 87.73% accuracy, 87.75% precision, 87.73% recall, 87.67% F1-score and 0.8359 kappa value. The high recall value of the model indicates that patients with HR can be successfully detected. Also, the high precision and F1-score values indicate that the overall accuracy of the model is balanced and the false positive rate is low. The Cohen's kappa value is as high as 0.83, which proves that the model performs much better than a random guessing classifier. MobileNet, the second most successful model, performed quite competitively with 86.40% accuracy, 86.60% precision, 86.40% recall, 86.31% F1-score and 0.8180 kappa value. In particular, the precision value of 86.60% indicates that most of the positive samples predicted by the model are indeed positive. The third best performing model, ResNet152, has a very high success rate with 85.87% accuracy, 86.01% precision, 85.87% recall, 85.83% F1-score and 0.8188 kappa value. The precision value of 86.01% shows that the model keeps the false positive rate low. The recall value of 85.87% indicates that most of the HR cases were correctly detected. The kappa coefficient of 0.81 proves that the model has a statistically significant success and provides significantly better results compared to random prediction. The models that performed less well include DenseNet121, ResNet101, VGG16, VGG19, Xception and InceptionV3. In particular, VGG16 and VGG19 showed acceptable results with an accuracy of 85.87% but exhibited a more uneven distribution compared to models with lower precision, recall and F1-score values. The fact that the VGG architecture is older and requires more parameters compared to advanced CNN architectures \begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Model** & **Accuracy** & **Precision** & **Recall** & **F1-Score** & **Kappa** \\ \hline DenseNet121 & 82.93 & 82.68 & 82.93 & 82.67 & 0.7717 \\ \hline
**DenseNet169** & **87.73** & **87.75** & **87.73** & **87.67** & **0.8359** \\ \hline DenseNet201 & 85.07 & 84.91 & 85.07 & 84.83 & 0.8002 \\ \hline InceptionV3 & 82.13 & 81.67 & 82.13 & 81.60 & 0.7616 \\ \hline
**MobileNet** & **86.40** & **86.60** & **86.40** & **86.31** & **0.8180** \\ \hline ResNet101 & 85.07 & 84.88 & 85.07 & 84.92 & 0.8001 \\ \hline ResNet101V2 & 85.33 & 85.25 & 85.33 & 85.00 & 0.8037 \\ \hline
**ResNet152** & **85.87** & **86.01** & **85.87** & **85.83** & **0.8188** \\ \hline ResNet152V2 & 83.20 & 83.14 & 83.20 & 82.91 & 0.7754 \\ \hline ResNet50 & 84.88 & 84.57 & 84.88 & 84.62 & 0.7965 \\ \hline ResNet50V2 & 80.27 & 79.64 & 80.27 & 79.42 & 0.7362 \\ \hline VGG16 & 85.87 & 85.38 & 85.07 & 85.06 & 0.8083 \\ \hline VGG19 & 85.87 & 85.64 & 85.87 & 85.41 & 0.8108 \\ \hline Xception & 84.27 & 84.18 & 84.27 & 84.13 & 0.7895 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Classification performance of CNN models evaluated on the test data. limited the generalization ability of the model and caused it not to be as successful as expected on the HR dataset. Remarkably, the InceptionV3 and Xception models performed lower than expected with 82.13% and 84.27% accuracy rates. Although these models are known for their ability to extract features at different scales, the low recall and F1-score values suggest that the generalization ability of the model is weakened by overfitting in certain classes. Especially Xception, despite its 84.27% accuracy rate, shows an uneven distribution in precision and recall values, indicating that the model is prone to misclassification. Figure 7 shows the confusion matrix of the three most successful CNN models. For 0 (Healthy), all models have a high accuracy rate. For 0 (Healthy), DenseNet169 predicted 91 correct predictions, while MobileNet and ResNet152 predicted 92 and 90 correct predictions for 0 (Healthy), respectively.