## 4 Performance Evaluation
### Classification Results
There may be performance differences when performing the same method on different datasets for specific reasons, i.e., background noise of a source image, illumination, occlusion, model overfitting, unrepresentative data samples, or the probability of the method. Inadequate evaluation of the model can lead to poor performing methods being pushed into production or suppressed under the assumption that the model is overfitted. The performance of the proposed approach is also determined utilizing the ROC curves and Confusion Matrix. The confusion matrix of DDSM, INbreast, and MIAS datasets is illustrated in Figure 10. AUC is an important quantitative metric in the ROC curve. The ROC curves were drawn for false positive (1-specificity) and true positive (sensitivity) rates controlling the thresholds of the obtained probability maps. The AUC computations \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Authors** & **Method** & **Accuracy (\%)** & **Specificity (\%)** & **Sensitivity (\%)** \\ \hline [PERSON] et al. [4] & CNN & 95.32 & N.A. & N.A. \\ \hline [PERSON] et al. [7] & AlexNet & 91.0 & N.A. & N.A. \\ \hline [PERSON] et al. [8] & AlexNet & 95.64 & N.A. & N.A. \\ \hline [PERSON] et al. [34] & AlexNet & 86 & N.A. & N.A. \\ \hline [PERSON] et al. [67] & AdaBoost & 87.93 & 97.73 & 57.20 \\ \hline
**Our Proposed Method** & TTCNN & **96.82** & **97.68** & **95.99** \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Performance comparison with respect to INbreastr database. Best values are emphasized in bold. N.A.—data not provided. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Authors** & **Method** & **Accuracy (\%)** & **Specificity (\%)** & **Sensitivity (\%)** \\ \hline [PERSON] et al. [64] & SVM & 95.5 & 96.65 & 95.81 \\ \hline [PERSON] et al. [65] & ELM & 96.02 & 94.32 & 95.29 \\ \hline [PERSON] et al. [68] & ANN & 61 & 77 & 95.03 \\ \hline [PERSON] et al. [69] & MLP & 93.37 & 92.43 & 94.18 \\ \hline [PERSON] et al. [70] & ANN & 94 & 96.67 & 90 \\ \hline
**Our Proposed Method** & TTCNN & **96.57** & **97.03** & **96.11** \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Performance comparison with respect to MIAS database. Best values are emphasized in bold. N.A.—data not provided. values are estimated for the DDSM, INbreast, and MIAS databsets. The ROC curve graph is illustrated in Figure 11.  
The results of breast cancer detection grading (with 95% confidence intervals) are presented in Table 12 that exhibits the data from aforesaid datasets (DDSM, INbreast, and MIAS). The proposed approach gives the PPV, F1, Jac, Mec, Er, and AUC of 98.96%, 99.06%, 98.19%, 98.16%, 0.92%, and 99.08% on DDSM, 98.96%, 99.06%, 98.19%, 98.16%, 3.17%, and 99.08% on INbreast, and 97.06%, 96.58%, 93.39%, 93.14%, 3.43%, and 96.57% on MIAS datasets, respectively. Over the past decade, non-invasive applications such as breast cancer detection and classification have become very prevalent among medical professionals, making the di  
Figure 11: Receiver operating characteristic (ROC) plot for DDSM, INbreast, and MIAS datasets. Figure 10: Confusion matrices for classification on DDSM, INbreast, and MIAS databases. agnostic process simpler and more accurate [71; 72]. TTCNN aims to improve clinical diagnosis by improving breast cancer detection and classification. Based on the accuracy produced by a certain algorithm, we obtained the opinion of two medical experts (one radiologists and one physician). These experts praised the improved results of TTCNN compared to other state-of-the-art approaches.