# Breast Cancer Diagnosis Using YOLO-Based Multiscale Parallel CNN and Flattened Threshold Swish
## 3 Results and Discussion
### Breast Cancer Detection Comparative Analysis  
The comparative analysis of the proposed YOLO-BCD framework against existing state-of-the-art BC detection schemes is detailed in Figure 5 and Table 4. Notably, Figure 5 illustrates that the proposed framework covers the maximum area under the PR-curve for both classes, indicating its superior balance between precision and recall over other frameworks. This is a critical indicator of the efficiency of a BC detection model, particularly in medical diagnostics, where both false positives and false negatives carry significant concerns. \begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Framework** & **Activation Function** & **Precision (\%)** & **Recall (\%)** & **F1-Score (\%)** & **Accuracy (\%)** \\ \hline Faster RCNN & ReLU & 84.36 & 85.23 & 84.70 & 87.80 \\ [PERSON] et al. [33] & & & & & \\ \hline YOLO V3 & & & & & \\ [PERSON] et al. [20] & Leaky ReLU & 90.94 & 89.83 & 90.36 & 92.16 \\ \hline YOLO V5 & & & & & \\ [PERSON] et al. [21] & Leaky ReLU & 92.08 & 91.31 & 91.67 & 93.72 \\ \hline YOLO-LOGO & & & & & \\ [PERSON] et al. [22] & Leaky ReLU & 95.53 & 95.47 & 95.50 & 96.51 \\ \hline YOLO-BCD (Proposed) & Leaky ReLU & 96.39 & 97.03 & 96.70 & 97.56 \\ \hline YOLO-BCD (Proposed) & FTS & 97.13 & 97.93 & 97.51 & 98.08 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance comparison of breast cancer classification using the INbreast dataset. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Multi-Scale Features** & **Multi-Level Features** & **CLAHE** & **Activation Function** & **Loss Function** & **Mass AP\%** & **Cal AP\%** & **mAP\%** \\ \hline \(\times\) & \(\bigvee\) & \(\bigvee\) & FTS & Scale-invariant & 87.17 & 86.98 & 87.08 \\ \hline \(\bigvee\) & \(\times\) & \(\bigvee\) & FTS & Scale-invariant & 88.44 & 87.59 & 88.02 \\ \hline \(\bigvee\) & \(\bigvee\) & \(\times\) & FTS & Scale-invariant & 89.14 & 88.17 & 88.66 \\ \hline \(\bigvee\) & \(\bigvee\) & \(\bigvee\) & FTS & YOLO Loss & 89.77 & 90.11 & 89.94 \\ \hline \(\bigvee\) & \(\bigvee\) & \(\bigvee\) & Leaky ReLU & Scale-invariant & 90.55 & 90.64 & 90.60 \\ \hline \(\bigvee\) & \(\bigvee\) & \(\bigvee\) & FTS & Scale-invariant & 90.95 & 91.35 & 91.15 \\ \hline \hline \end{tabular} (\(\times\)) represents feature exclusion; (\(\bigvee\)) represents feature incorporation.  
\end{table} 
Table 3: Ablation Experiment using the CBIS-DDSM dataset. Table 4 further reveals this point by presenting the Average Precision (AP) for each class and the mean Average Precision (mAP) across different models. The proposed YOLO-BCD framework demonstrates the highest average precision scores using the FTS activation function for both Mass (90.95%) and Calcification (Cal) (91.35%) detection, leading to an impressive mean Average Precision of 91.15%. This surpasses other models, such as the Faster RCNN by [PERSON] et al. [33], with an mAP of 79.30%, the YOLO V3-based model by [PERSON] et al. [20], with an mAP of 78.76%, YOLO V5 by [PERSON] et al. [21], with an mAP of 83.84%, and YOLO-LOGO by [PERSON] et al. [22], with an mAP of 88.60%. These results collectively underscore the superior accuracy of the YOLO-BCD framework in detecting breast cancer indicators. In Figure 6, some qualitative results are presented to demonstrate the detection of breast cancer in test images from the CBIS-DDSM dataset. \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Framework** & **Activation Function** & **Mass AP\%** & **Cal AP\%** & **mAP\%** \\ \hline Faster RCNN & & & & \\ [PERSON] et al. [33] & ReLU & 80.94 & 77.66 & 79.30 \\ YOLO V3 & & & & \\ [PERSON] et al. [20] & Leaky ReLU & 77.83 & 79.70 & 78.76 \\ YOLO V5 & & & & \\ [PERSON] et al. [21] & Leaky ReLU & 85.49 & 82.18 & 83.84 \\ YOLO-LOGO & & & & \\ [PERSON] et al. [22] & Leaky ReLU & 88.31 & 88.89 & 88.60 \\ YOLO-BCD (Proposed) & FTS & 90.95 & 91.35 & 91.15 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance comparison using the CBIS-DDSM dataset.