## 4 Experimental Results and Analysis
### Model Evaluation  
We used the accuracy metrics provided by _TensorFlow.Keras_. The accuracy represents how often the predicted values match the labels. Figure 4 shows the evaluation results in terms of accuracy and loss percentages for the MI-AUC and State Farm datasets. While the test accuracy shows how well the model matches the testing data, the training accuracy shows how well it fits the training data. The testing loss evaluates the inaccurate prediction of the model on the testing set and shows the disparity between the model and the testing data. The training loss evaluates the inaccuracy of the models, it demonstrates the disparity between the model and the training set. Note that the training set represents 80% of the dataset initially used to train the model. The testing accuracy curve is close enough to the training accuracy curve. In addition, we can notice that the U2-net model achieves high training and testing accuracy values. Further, the U2-net model achieves better results when using the State Farm datasets. Moreover, we notice that the U2-Net model starts to converge at early stages (i.e., when epochs \(<\) 5). Table 4 shows the training accuracy, testing loss, training loss, and testing accuracy for U2-net compared to other architectures using the State Farm datasets. The results in the first four rows are collected from Ref. [7]. The authors in Ref. [7] have implemented and evaluated these architectures using the same datasets that we used--State Farm. They also used the same training and testing ratios (80% for training and 20% for testing). The last row shows the evaluation results of the U2-net on the State Farm datasets. We notice that the U2-net achieves the best results regarding accuracy and loss values. It achieves 100% training accuracy and 99.64% testing accuracy (1.52 higher than MobileNetV2 [7]). \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Model** & **Training Loss** & **Training Accuracy** & **Testing Loss** & **Testing Accuracy** \\ \hline Simple CNN [7] & 0.147 & 96.09\% & 0.1348 & 96.76\% \\ VGG-16 [7] & 0.00185 & 97.68\% & 0.242 & 93.23\% \\ ResNet50 [7] & 0.0597 & 98.62\% & 0.26795 & 94.69\% \\ MobileNetV2 & 0.0035 & 99.83\% & 0.2057 & 98.12\% \\
**U2-net (proposed model)** & **0.000081** & **100.0\%** & **0.02** & **99.64\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Training and testing accuracy for U2-NET compared to other architectures using the State Farm dataset [7]. We also compare the U2-net model's outcomes with the state-of-art CNN architectures from collected works, as shown in Table 4. The U2-net model achieves the best performance--98.34% (2.75%, 2.36%, and 2.03% higher accuracy than D-HCNN [6], GA-Weighted Ensemble, and VGG- with Regularization, respectively). In addition, Table 4 shows the approximate number of parameters for each model; the D-HCNN [6] has the lowest number of parameters (0.76 million). However, the U2-net achieves 2.75% higher accuracy than the D-HCNN model. This research focuses on enhancing the performance in terms of accuracy while enhancing the performance in terms of time complexity (considered as future work). Suppose we observe the accuracy results from Tables 4 and 5. In that case, we can notice a gap between results generated from applying the U2-net on MI-AUC and results generated from applying the U2-net on the State Farm dataset. This gap is due to the incorrect training labels and testing data provided in the MI-AUC dataset, while the data in the State Farm dataset is accurately labeled [41]. The confusion matrix is a particular design that makes it possible to see how well a classification performs. Examples in a real class are represented in each row of the matrix, whereas those in a predicted class are represented in each column. Since the matrix's diagonal contains all the accurate predictions, it is simple to examine the accuracy directly: the numbers outside the diagonal correspond to the incorrect predictions [42].