## 5 Results
### Cutting-Edge Comparisons
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Study** & **Segmentation** &
\begin{tabular}{c} **Feature** \\ **Selection** \\ \end{tabular} & **Methods** & **Accuracy** & **Sensitivity** & **Specificity** & **Precision** & **F1-Score** \\ \hline
[36] & U-Net & No & Customized CNN & 0.9933 & 1.00 & 0.9867 & \\ \hline
[27] & \
\begin{tabular}{c} Fuzzy C-means \\ Clustering \\ \end{tabular} & No &
\begin{tabular}{c} Customized \\ EDCNN \\ \end{tabular} & 0.9680 & & 0.9370 & \\ \hline
[43] &
\begin{tabular}{c} Temperature \\ Ranges \\ \end{tabular} & No & Customized CNN & 0.9385 & 0.9053 & 0.9700 & 0.9666 \\ \hline
[29] & No & No &
\begin{tabular}{c} VGG16 + \\ Sequential \\ Classifier \\ \end{tabular} & 0.9940 & 1.00 & 0.9750 & 0.9890 & 0.9980 \\ \hline
[40] & No & No & Customized CNN & 0.970 & 1.00 & 0.830 & \\ \hline
[65] & \
\begin{tabular}{c} level-set \\ segmentation \\ \end{tabular} & No &
\begin{tabular}{c} GLCM + GLRM + \\ GLSZM + NGTDM \\ + PCA + SVM \\ \end{tabular} & 0.960 & 1.00 & 0.920 & \\ \hline
[42] &
\begin{tabular}{c} Canny Edge \\ Detector + \\ Morphological \\ Operations + \\ \end{tabular} & No & AlexNet & 0.9048 & 0.9333 & 0.8333 & 0.9333 \\ \hline
[30] &
\begin{tabular}{c} Morphological \\ operation + \\ object-oriented \\ segmentation \\ \end{tabular} & No & Customized CNN & 0.9895 & 0.9828 & 0.9959 & 0.9956 \\ \hline
[64] & No & Yes &
\begin{tabular}{c} ResNet34 + \\ Chi-square + SVM \\ \end{tabular} & 0.9962 & 0.9963 & & 0.9963 & 0.9963 \\ \hline
**Proposed** & No & Yes &
\begin{tabular}{c} MobileNet, \\ EfficientNetB0, \\ and ShuffleNet + \\ NNMF + SVM \\ \end{tabular} & 0.9990 & 0.9980 & 1.000 & 1.000 & 0.9990 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Cutting-edge comparisons of the suggested CAD with related works. minimizes architecture-specific biases, guaranteeing the model incorporates a wider array of thermal patterns linked to breast cancer. Furthermore, the suggested CAD approach demonstrates exceptional accuracy and excels in essential clinical metrics. The model exhibits 100% specificity and precision, entirely eradicating false positives which is not the case in studies [29; 36; 64]. This represents a significant improvement in breast cancer screening, as false positives can result in unneeded biopsies, greater anxiety among patients, and elevated healthcare expenses. Furthermore, the model's sensitivity of 99.8% guarantees precise detection of true positives, thereby reducing the likelihood of overlooked diagnoses. Although the absolute accuracy enhancements compared to previous studies may seem marginal, the consistent equilibrium across all performance metrics (e.g., 100% specificity vs. 97.5% in [29]) underscores the framework's resilience and clinical dependability. The proposed methodology also removes superfluous preprocessing phases, including intricate segmentation algorithms utilized in studies such as [27; 30; 36; 42; 65]. By optimizing the workflow and concentrating on feature extraction and classification, the study diminishes computational demands while maintaining diagnostic efficacy. The efficiency, coupled with the utilization of lightweight CNNs, guarantees that the proposed CAD system is practical and scalable for implementation in various clinical environments. An essential novelty of the proposed CAD system resides in the multi-layer feature extraction approach. In contrast to traditional methods that exclusively utilize attributes from the final layer of a singular network, the proposed methodology extracts and integrates features from three separate layers of each CNN. This guarantees the integration of both detailed thermal variations from initial layers and overarching spatial dependencies from deeper layers into the classification process. To improve feature quality and minimize computational demands, the proposed framework utilizes sophisticated feature transformation methods including DWT and NNMF. These approaches separate physiologically pertinent thermal patterns while reducing redundancy and noise.