# Hybrid Optimization and Explainable Deep Learning for Breast Cancer Detection
## 2 Proposed Methodology
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Ref.** & **Methodology** & \
\begin{tabular}{c} **Dataset or Application** \\ **Context** \\ \end{tabular} & **Major Findings** & **Noted Limitations** &
\begin{tabular}{c} **Distinctive Contribution** \\ **Relative to the Present Work** \\ \end{tabular} \\ \hline
[45] &
\begin{tabular}{c} Snake-spline \\ segmentation + \\ Flower Pollination \\ Optimization \\ \end{tabular} & CT lung cancer dataset & Accuracy: 84\% using 33 features & Focused on lung CT, not BC & Hybrid segmentation and FS  using FFO for SVM \\ \hline
[46] & \
\begin{tabular}{c} Bacterial Foraging + \\ Emperor Penguin \\ Optimization (EPO), \\ hGSAEPO \\ \end{tabular} & WDBC BC dataset & Accuracy: 98.31\%, AUC \textgreater{} 0.998 &
\begin{tabular}{c} Computational \\ complexity of \\ hybrids \\ \end{tabular} & High-performing hybrid FS + XAI integration \\ \hline
[47] & \begin{tabular}{c} Liver Cancer \\ Algorithm (LCA) + SVM optimization \\ \end{tabular} & \begin{tabular}{c} MonoAmine Oxidase \\ (MAO) dataset \\ \end{tabular} & Accuracy: 98.704\% & \
\begin{tabular}{c} Not breast \\ cancer-specific \\ \end{tabular} &
\begin{tabular}{c} Novel evolutionary \\ optimization inspired by tumor \\ dynamics \\ \end{tabular} \\ \hline
[48] & \begin{tabular}{c} Wrapper-based \\ FS + RF, SVM, DT \\ \end{tabular} & \begin{tabular}{c} Early BC \\ biomarker detection \\ \end{tabular} & \begin{tabular}{c} AUC: 0.89-0.98, \\ Sensitivity: 0.94, \\ Specificity: 0.90 \\ \end{tabular} & \
\begin{tabular}{c} Requires feature \\ engineering \\ \end{tabular} &
\begin{tabular}{c} Sequential backward FS \\ enhancing early \\ biomarker identification \\ \end{tabular} \\ \hline
[49] & \begin{tabular}{c} DeepLabV34 + \\ Autoencoder + \\ Grad-CAM + GLCM \\ \end{tabular} & Breast ultrasound datasets & \begin{tabular}{c} Accuracy: 97.4\%, \\ Dice: 0.981 \\ \end{tabular} & \
\begin{tabular}{c} Specific to \\ ultrasound; not \\ multimodal \\ \end{tabular} &
\begin{tabular}{c} Strong segmentation and \\ interpretability using XAI \\ \end{tabular} \\ \hline
[51] & \begin{tabular}{c} Random Forest + \\ SHAP (XAI) + \\ Clinical features \\ \end{tabular} & \begin{tabular}{c} NSLN metastases \\ prediction \\ \end{tabular} & \begin{tabular}{c} Accuracy: 72.2\%, \\ AUC: 0.77 \\ \end{tabular} & \
\begin{tabular}{c} Limited dataset \\ (183 cases) \\ \end{tabular} &
\begin{tabular}{c} Interpretable model using \\ SHAP for clinical guidance \\ \end{tabular} \\ \hline
[52] & \begin{tabular}{c} CNN (VGG16, \\ VGG19, ResNet) + \\ XAI (LIME, SHAP, \\ Saliency Maps) \\ \end{tabular} & Histopathology images & Accuracy: 92.59\% & \
\begin{tabular}{c} Downsampled input, \\ lacks real-time \\ validation \\ \end{tabular} &
\begin{tabular}{c} Evaluation of XAI methods for \\ clinical interpretability \\ \end{tabular} \\ \hline
[53] & \begin{tabular}{c} Patho-Net (GRU + \\ U-Net + GLCM + \\ XAI) \\ \end{tabular} & \begin{tabular}{c} BreakHis 100X \\ histopathology \\ \end{tabular} & Accuracy: 98.90\% & \
\begin{tabular}{c} Focused on \\ histopathology only \\ \end{tabular} &
\begin{tabular}{c} End-to-end DL with built-in \\ interpretability and \\ preprocessing \\ \end{tabular} \\ \hline
[54] & \begin{tabular}{c} CNNs + \\ Grad-CAM, \\ Grad-CAM++ \\ \end{tabular} & Mammography images & \begin{tabular}{c} DenseNet201: \\ Accuracy 99\% \\ \end{tabular} & \
\begin{tabular}{c} Limited multiclass \\ analysis \\ \end{tabular} &
\begin{tabular}{c} Comprehensive use of multiple \\ XAI methods \\ \end{tabular} \\ \hline
[55] & \begin{tabular}{c} 3D tomosynthesis + \\ Grad-CAM + LIME + \\ t-SNE + UMAP \\ \end{tabular} & Lesion-region images & AUC: 98.2\% & \
\begin{tabular}{c} High-dimensional \\ visualization needed \\ \end{tabular} &
\begin{tabular}{c} Structural + visual \\ interpretability in multiclass \\ BC diagnosis \\ \end{tabular} \\ \hline
[56] & \begin{tabular}{c} Condensed CNN \\ \end{tabular} & MIAS, DDSM, Combined & \begin{tabular}{c} Accuracy: 96.67\% \\ (combined dataset) \\ \end{tabular} & \
\begin{tabular}{c} Limited XAI \\ integration \\ \end{tabular} &
\begin{tabular}{c} Efficient CNN optimized for \\ performance and speed \\ \end{tabular} \\ \hline
[57] & \begin{tabular}{c} ResNet50 + \\ Improved Marine \\ Predators Algorithm \\ (IMPA) \\ \end{tabular} & MIAS, CBIS-DDSM & Accuracy: 98.88\% & \
\begin{tabular}{c} Dataset-specific \\ performance \\ \end{tabular} &
\begin{tabular}{c} Metaheuristic TL optimization \\ with opposition learning \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}