## 6 Results
### Classification Results  
Experiments were conducted to classify the thermal images of breast cancer through the proposed models using the CNN model with/without the self, hard, and soft AMs (CNN, CNN-SL, CNN-HD, CNN-SF). We resized all images to 56 \(\times\) 56 pixels, which is the resolution often utilized in CNN applications [25]. Furthermore, we used stratified 10-fold cross-validation to ensure that each image was evaluated in both the test and training sets. Additionally, it minimized generalization issues introduced by overfitting. The performance of the proposed models was evaluated on the DMR-IR dataset, and the results were compared to determine the most accurate approach. Table 3 shows the accuracy, specificity, sensitivity, AUC ROC, and Cohen's kappa scores of each model. Table 3 shows the average results of the CNN with/without AMs to observe the effect on the performance of the CNN model. The CNN model without the use of AMs achieved the lowest performance among the models; nevertheless, it showed good results. We found that using AMs with the CNN model had a high impact on the improved performance of the CNN in all metrics. The results also showed a convergence between three AM models with the CNN. The CNN-HD indicated that the use of hard attention with the CNN had a slightly higher effect on metrics such as accuracy, specificity, sensitivity, and F1-score. The CNN-SF performed higher than other models in the precision metric. As we know, recall is a critical measure in medical applications; accordingly, the proposed model results achieved encouraging results. \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Dataset** & **Size** & **Normal Breasts** & **Abnormal Breasts** \\ \hline DMR-IR-Original & 1542 & 762 & 780 \\ DMR-IR-Augmented & 2604 & 1284 & 1320 \\ \hline Total & 4146 & 2046 & 2100 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Dataset details. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Models** & **Accuracy (\%)** & **Specificity** & \begin{tabular}{c} **Sensitivity** \\ **(\%)** \\ \end{tabular}  & \ \begin{tabular}{c} **Precision** \\ **(\%)** \\ \end{tabular}  & **F1-Score (\%)** & **AUC** & \begin{tabular}{c} **Cohen’s** \\ **Kappa** \\ \end{tabular}  \\ \hline CNN & 84.92\% & 89.61\% & 89.61\% & 90.23\% & 83.91\% & 0.851 & 0.69 \\ CNN-SL & 99.32\% & 99.52\% & 99.52\% & 99.14\% & 99.32\% & 0.999 & 0.98 \\ CNN-HD & 99.49\% & 99.71\% & 99  
Furthermore, we offered the results of the average AUC ROC of 10-fold cross-validation, where the AUC score represents the ability of the model to distinguish between classes, in our case, normal and cancer. The CNN models with the three AMs achieved convergence in results, which were about 0.999, while the CNN was 0.851. Figure 2 below plots the ROC curves of four models for each fold and shows the AUC results and average. [PERSON]'s kappa is a robust statistic that can be utilized for intra- and inter-rater reliability evaluation. Similar to correlation coefficients, it can have values between \(-1\) and \(+1\), where 0 denotes the degree of agreement predicted by chance and 1 denotes perfect agreement between the raters. The kappa is a standardized value interpreted in the same way across various studies, such as in all correlation statistics. [PERSON] recommended using  
Figure 2: ROC curves of cross-validation results of four models as: (**a**) CNN model; (**b**) CNN-SL model; (**c**) CNN-HD model; (**d**) CNN-SF model. the following scale to interpret the kappa result: values \(\leq 0\) indicating no agreement, and 0.01-0.20 as none to slight, 0.21-0.40 as fair, 0.41-0.60 as moderate, 0.61-0.80 as substantial, and 0.81-1.00 as almost perfect agreement [37]. The CNN models with the three AMs presented a great performance in this metric, achieving 0.98 for all mechanisms, which represented almost perfect agreement. The result of the CNN alone was 0.69, which represented substantial agreement. We provided aggregate [PERSON]'s kappa scores of 10-fold cross-validation.