## 4 Results and Discussion  
### Performance Evolution  
To evaluate the performance of the models utilized in this study, we used accuracy, precision, recall and F1-score. Which may be calculated as follows:  
\[Accuracy=\frac{TP+TN}{TP+FP+TN+FN} \tag{4}\]  
\[precision=\frac{TP}{TP+FP} \tag{5}\]  
\[\text{Re}\;call=\frac{FN}{FN+TP} \tag{6}\]  
\[F1-Score=2*\frac{\left(precision\bullet\text{Re}\;call\right)}{\left(precision+ \text{Re}\;call\right)} \tag{7}\]  
True Positive (TP): The positive data anticipated properly is estimated. The diagonal is the most important value. True Negative (TN): Negative data was assessed to be negative. Except for the row and column of the associated class, it is the sum of all values in the confusion matrix. False Positive (FP): Negative data was assessed to be positive. For each class, it is the total of all values in the relevant column except TP. False Negative (FN): Positive data is assessed to be negative. For each class, it is the total of all values in the relevant row except TP. Performance Evaluation of DeepBreastCancerNet  
In this experiment, chest radiograph images are used to test the proposed DL model's detection (three-class classification) performance. The dataset was split into training and testing sets for this experiment, with 30% of the data being used for model testing and 70% being utilized for training. More specifically, we employed all 1030 BC ultrasound pictures (537 benign, 360 malignant, and 133 normal), of which 376 were used for training, 252 were used for testing, and 95 were of benign, malignant, and normal persons. The parameters presented in Table 2 are used in the training set to practice the proposed framework's BC detection and classification. The proposed DL model underwent 700 total iterations, averaging iterations per epoch throughout the course of the 7 epochs. The suggested DeepBreastCancerNet achieved the maximum classification accuracy, precision, recall, and F1-score values 99.35%, 99.60%, 99.66% and 99.60% at epoch 100, respectively, demonstrating the effectiveness of our method for detecting BC. Additionally, we've shown accuracy and loss in Figure 3 to demonstrate how well the proposed methodology performs throughout training and testing. How successfully the framework can predict the dataset is shown by the loss function. The loss and accuracy of our model after epoch 55 roughly remain the same, demonstrating that it is still more accurate at forecasting BC at lower epochs than 100. Figure 4 depicts the proposed DL approach's training and validation procedure as well as the confusion matrix for the testing phase of the Breast cancer detection framework for DL detection. The suggested Model classified incorrectly just 2 images. Figure 5. The proposed DL framework's training and validation loss (black line shows testing loss whereas red line shows training loss). Furthermore, a detailed training parameters ablation study was conducted to demonstrate the validity of the proposed DL approach we have trained the proposed model with 20% of the data being used for model testing and 80% being utilized for training. The proposed DL model achieved the maximum classification accuracy, precision, recall, and F1-score values 98.50%, 98%, 98.50% and 98% respectively Moreover, we used different training parameters we used a 10-image minibatch size and a 0.01 learning rate. Additionally, DL model was trained for 50 epochs before being for identifying and classifying different forms of BC. The suggested DeepBreastCancerNet achieved the maximum classification accuracy, precision, recall, and F1-score values 97.50%, 97%, 97.50% and 97%respectively. Hence, it's been noticed that decreasing the number of epochs and change the data split effect the performance of the proposed model. This indicates how our suggested strategy can increase the accuracy of BC identification and classification from ultrasound images.