## 4 Performance Evaluation
### Classification Results  
The proposed TTCNN method classifies mammogram images of breast tumors as benign or malignant. The experiments are performed on DDSM, INbreast, and MIAS databases. The classification accuracy of the proposed TTCNN approach using three different databases is illustrated in Table 8. The total training samples of 287 benign and 302 malignant are selected and the total testing samples of 192 benign and 200 malignant are selected for the DDSM dataset and obtained an accuracy of 99.08%. Similarly, the total training samples selected for INbreast are 132 benign and 29 malignant while testing samples are 88 benign and 20 malignant and achieved an accuracy of 96.82%. The total training samples of 40 benign and 31 malignant are selected and the total testing samples of 28 benign and 20 malignant are selected for the MIAS dataset and achieved an accuracy of 96.57%. The quantitative comparison of the proposed TTCNN method is also compared with other existing state-of-the-art algorithms for each database. The proposed approach appears to outperform other state-of-the-art approaches with high values of accuracy, specificity, sensitivity, and F1 score as illustrated in Tables 9-11. The best value is highlighted in bold text. \begin{table}
\begin{tabular}{l c c c c c} \hline \multirow{2}{*}{**Database**} & \multicolumn{2}{c}{**Total Number of Images**} & \multicolumn{2}{c}{**Correctly Detected**} & \multirow{2}{*}{**Accuracy (\%)**} \\  & **Benign** & **Malignant** & **Benign** & **Malignant** & \\ \hline DDSM & 479 & 502 & 474 & 498 & 99.08 \\ INbreast & 220 & 49 & 215 & 47 & 96.82 \\ MIAS & 68 & 51 & 66 & 49 & 96.57 \\ \hline \end{tabular}
\end{table} 
Table 8: Summary of breast cancer detection. \begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**Authors**} & **Method** & **Accuracy (\%)** & **Specificity (\%)** & **Sensitivity (\%)** \\ \hline [PERSON] et al. [22] & Ensemble & 93.1 & 88 & 95 \\ \hline [PERSON] et al. [45] & MFO-ELM & 98.8 & 97.22 & 96.2 \\ \hline [PERSON] et al. [64] & ANN & 98.18 & N.A. & N.A. \\ \hline [PERSON] et al. [65] & ELM & 95.73 & 97.16 & 94.88 \\ \hline [PERSON] et al. [66] & WC-SSA & 98.63 & 97.37 & 98.77 \\ \hline [PERSON] et al. [67] & AdaBoost & 90.91 & 97.38 & 82.96 \\ \hline
**Our Proposed Method** & TTCNN & **99.08** & **98.96** & **99.19** \\ \hline \end{tabular}
\end{table} 
Table 9: Performance comparison with respect to DDSM database. Best values are emphasized in bold. N.A—the data is not provided. Table 9 illustrates the quantitative comparison of our proposed approach with existing state-of-the-art algorithms for the DDSM database. The proposed method results yielded superior performance for the DDSM dataset and achieved an accuracy of 99.08%, specificity of 98.96%, and sensitivity of 99.19%. [PERSON] et al. [66] also exhibit better performance than the remaining methods as it has the accuracy of 98.63%. However, our approach has better accuracy, specificity, and sensitivity when compared with other algorithms as indicated by the bold text. Table 10 illustrates the comparison for the INbreast database where the proposed approach showed enriched performance and attained an accuracy of 96.82%, specificity of 97.68%, and sensitivity of 95.99%. and outperformed other methods quantitatively. The accuracy result obtained by [PERSON] et al. [8] is slightly better than the other approaches which is 95.64%. But still the proposed approach exhibits best performance for the INbreast database when compared with other approaches. Table 11 displays the comparison for the MIAS database where the proposed approach still reveals superior performance and surpasses all the existing state-of-the-art approaches and achieved an accuracy of 96.57%, specificity of 97.03%, and sensitivity of 96.11%. The proposed approach improves breast cancer detection and classification performance in comparison. The proposed approach can be utilized for real-time assessment and assist radiologists for automated analysis of mammogram images.