## 6 Discussion
### Comparative Performance Analysis
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{2}{c}{**Methods**} & \multicolumn{1}{c}{**Number of Features**} & \multicolumn{1}{c}{**Precision**} & \multicolumn{1}{c}{**Accuracy**} & \multicolumn{1}{c}{**Sensitivity**} & \multicolumn{1}{c}{**F1-Score**} \\ \hline \multirow{4}{*}{[69]} & \multicolumn{3}{c}{**Preprocessing:**} & \multirow{4}{*}{1000} & \multirow{4}{*}{0.9897} & \multirow{4}{*}{0.9927} & \multirow{4}{*}{0.9763} & \multirow{4}{*}{0.9829} \\  & & & & & & \\ \cline{1-1} \cline{5-6}  & Cropping the images into multiple & & & & & \\ \cline{1-1} \cline{5-6}  & sub-images & & & & & \\ \cline{1-1} \cline{5-6}  & \multicolumn{1}{c}{**Processing:**} & \multirow{4}{*}{1000} & \multirow{4}{*}{0.9897} & \multirow{4}{*}{0.9927} & \multirow{4}{*}{0.9763} & \multirow{4}{*}{0.9829} \\  & DarkNet-19 + NCA + SVM & & & & & \\ \hline \multirow{2}{*}{[70]} & VGG-16 + Inception + DenseNet-121 + ResNet-50 + PCA + GWO & 796 & 0.9914 & 0.9947 & 0.9927 & 0.9920 \\ \hline \multirow{2}{*}{[71]} & MobileNet + Inception + InceptionResNet & N/A & 0.9934 & 0.9968 & 0.9934 & 0.9987 \\ \hline \multirow{2}{*}{[72]} & Inception + Xception + DenseNet-169 + Fuzzy rank & N/A & 0.9913 & 0.9923 & 0.9923 & 0.9918 \\ \hline \multirow{2}{*}{[73]} & Tam Transformers + Visual & \multirow{2}{*}{N/A} & \multirow{2}{*}{-} & \multirow{2}{*}{0.9879} & \multirow{2}{*}{0.9861} & \multirow{2}{*}{-} \\  & Transformers & & & & & \\ \hline \multirow{2}{*}{[74]} & GoogleNet + ResNet-18 + Genetic & \multirow{2}{*}{730} & \multirow{2}{*}{0.9839} & \multirow{2}{*}{0.9907} & \multirow{2}{*}{0.9818} & \multirow{2}{*}{0.9831} \\  & Algorithm + SVM & & & & & \\ \hline \multirow{2}{*}{Proposed} & Multiple DL and Handcrafted & \multirow{2}{*}{35} & \multirow{2}{*}{1} & \multirow{2}{*}{1} & \multirow{2}{*}{1} & \multirow{2}{*}{1} \\  & Features + PCA & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparative analysis between state-of-the-art methods and the introduced CAD constructed with the Mendeley LBC dataset. periority of the proposed CAD is due to using multiple compact CNNs to extract deep features which is not the case in the studies [69; 70; 71; 72; 73; 74], which employed individual CNNs with more deep layers and huge parameters. Additionally, it combined features from multiple domains including time/spatial and time-frequency domains in contrast to methods that relied on extracting features from a single domain [69; 70; 71; 72; 73; 74]. In addition, it did not necessitate the use of any pre-segmentation process to reach accurate results like the study [69]. Furthermore, it accomplished better results than studies [69; 70; 74] which used complicated methods to reduce features like genetic algorithms. These results were attained even with a lower number of features (35 PCs) compared to 1000, 796, and 730 used in [69; 70; 74]. In addition, the results show that merging multiple DL features from several CNNs with different handcrafted features from multiple domains utilized by the proposed CAD is better than using features from a single domain as done by previous studies [69; 70; 71; 72; 73; 74].