## 5 Results  
The results section delineates the findings of the suggested CAD system across four different evaluation scenarios aimed at assessing its efficacy in breast cancer identification. The initial scenario examines the contribution of features gathered from each layer of the three deep neural networks to identification accuracy. This analysis clarifies the contribution of each feature set to model performance. The second scenario assesses the influence of different quantities of features derived from the NNMF dimensionality reduction method on classification performance, particularly on features derived from Layers 1 and 2. This scenario evaluates the balance between dimensionality reduction and predictive accuracy by examining various feature subset lengths. The third scenario examines the advantages of incorporating features from deep neural networks with various structures to ascertain if the integration of attributes from disparate networks improves overall identification performance. Finally, the fourth scenario investigates the impact of consolidating features derived from various layers across the three compact deep neural networks. This involves identifying the most important attributes via selection procedures, thereby diminishing feature redundancy, reducing model complexity, and decreasing training duration. Such investigations together offer an in-depth comprehension of how different configurations of feature sets and combinations affect the performance and effectiveness of the CAD system.  
Diverse assessment indicators are employed to evaluate the performance of the suggested CAD, guaranteeing a thorough comprehension of its efficacy. The metrics encompass accuracy, F1 score, precision, specificity, sensitivity, and Matthew's correlation coefficient (MCC). Every metric offers different perspectives on the model's classification accuracy, equilibrium between precision and recall, and capacity to manage imbalanced datasets. The computations for those indicators rely on the Equations (9)-(15). The assessment mechanism also employs confusion matrices, providing a comprehensive analysis of true positives, true negatives, false positives, and false negatives. This analysis aids in comprehending the model's decision-making process and pinpointing areas for enhancement.  
Additionally, receiver operating characteristic (ROC) curves and the area under this curve (AUC) are produced to assess the balance between sensitivity and specificity at various classification thresholds, offering a visual depiction of the model's discriminative capacity. These assessment techniques comprehensively assess the performance, reliability, and robustness of the suggested CAD.  
\[Sensitivity=\frac{TP}{TP+FN} \tag{9}\]  
\[Specificity=\frac{TN}{TN+FP} \tag{10}\]  
\[Precision=\frac{TP}{TP+FP} \tag{11}\]  
\[MCC=\frac{TP\times TN-FP\times FN}{\sqrt{(TP+FP)(TP+FN)(TN+FP)(TN+FN)}} \tag{12}\]  
\[F\_1-score=\frac{2\times TP}{(2\times TP)+FP+FN} \tag{13}\]  
\[Accuracy=\frac{TP+TN}{TN+FP+FN+TP} \tag{14}\]  
\[AUC=\sum TP+\sum\frac{TN}{P+N} \tag{15}\]  
True Negatives (TN) denote examples that are properly categorized as negative, indicating they belong to the negative class (N) and are accurately recognized to be so by the model. True Positives (TP) refer to occurrences successfully classified as belonging to the positive class (P), reflecting the model's proficiency in identifying positive occurrences. Conversely, False Negatives (FN) arise when positive examples are erroneously classified as negative, indicating a failure of the model to identify true positive cases. False Positives (FP) occur when negative examples are erroneously classified as positive, indicating a miscalculation of the positive class by the model.