## 5 Results and Discussion  
The presentation of our research findings is structured into two distinct parts, both offering valuable insights into the performance and characteristics of the applied methodologies and conducted experiments. In the first segment, we explore the effectiveness of metaheuristic algorithms with the novel hybrid approach HSMO and HPSO coupled with feature extraction methods. The aim was to evaluate the consistency of results across diverse optimization strategies. Multiple experiments were tested on the DMR-IR dataset. The positive class represents instances of sick breasts (red color). Therefore, to optimize \begin{table}
\begin{tabular}{c c c c} \hline \hline
**CAD Steps** & **Methods** & **Feature Variables** & **Parameter Values *** \\ \cline{3-4}  & LBP & Radius & 8; [2, 24]: 2 \\ \cline{3-4}  & Points & 60; [100, 550]: 10 \\ \cline{3-4}  & & Orientations & 9; [9, 18]: 1 \\ \cline{3-4}  & HOG & Pixels per cell & 8; [2, 16]: 1 \\ \cline{3-4}  & Cells per block & 4; [2, 5]: 1 \\ \cline{3-4}  & & K-size: Size of the Gabor kernel. Larger values capture more & \\  & & spatial frequencies but also increase & 80; [20, 190]: 20 \\  & computational complexity. & & \\ \cline{3-4}  & & Sigma: Standard deviation of the Gaussian envelope. It & 2.5; [0.10, 7]: 0.10 \\ \cline{3-4}  & & controls the spread of the filter. & 40; [0.1, 46]: 2 \\ \cline{3-4}  & Feature & Gabor Filter & Theta: Orientation of the normal to the parallel stripes of a & 40; [0.1, 46]: 2 \\ Extraction & & Gabor function. It determines the orientation of the features & 1.6; [0.2, 8]: 0.5 \\ \cline{3-4}  & & lambda: Wavelength of the sinusoidal factor. It affects the frequency of the feature to be detected. & 90; [8, 250]: 0.5 \\ \cline{3-4}  & & Gamma: Spatial aspect ratio. It controls the ellipticity of & 90; [8, 250]: 0.5 \\ \cline{3-4}  & & the filter. & 40; [7, 80]: 1 \\ \cline{3-4}  & & Aperture transition: Represents the transition range for & 2; [2, 10]: 2 \\ \cline{3-4}  & Canny Edge & Aperture transition steps: This parameter specifies the & \\  & & number of steps or iterations used for adjusting the aperture & 60; [10, 120]:1 \\ \cline{3-4}  & & transition range. It determines the granularity of the & \\  & & optimization process for the aperture transition. & \\ \hline Feature Selection & The number of & & True for included/False \\ \cline{3-4}  & selected features by & 128 Binary variables & for not-included feature \\ \hline Classification & SVM & C: Regularization parameter. Controls the trade-off between & 100; [10, 7500]: 10 \\ \cline{3-4}  & & training error and margin. & 100; [10, 7500]: 10 \\ \cline{3-4}  & & Coef0: Independent term in the kernel function. & 4; [\(1\times 10^{-6}\), 4]: 0.5 \\ \cline{3-4}  & & Tolerance: determines the convergence criterion for the & \(1\times 10^{-6}\); [\(1\times 10^{-6}\), 4 \(\times 10^{-2}\)]: \(9\times 10^{-4}\) \\ \cline{3-4}  & & Degree: represents the degree of the polynomial function & \\  & used in the kernel. & 3; [2, 10]: 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Details about the method used in different stages with arguments and hyperparameters range. the balance between computational resources and the desired level of performance, it was deemed sufficient to limit the optimization process to 60 iterations, as the incremental benefits beyond this point were deemed negligible. The experiment was conducted using Google Colab, powered by a Tesla4 GPU accelerator. Figure 10 shows a chart indicating the resulting reduction in samples, as well as what the reduction translates to in terms of a visual interpretation of the LBP, HOG, Gabor, and edge features. It can be seen that the histogram of the feature importance distribution using HSMO and HPSO show similar shapes. In the case of an HOG extractor, the feature counts range between 0 and 0.27. The highest peaks occur within bins 13 to 25 for both healthy and sick instances.