## 9 Key Observations and Recommendations
### Machine Learning Flowchart
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Method/Architecture** & **Advantages** & **Disadvantages** & **Recommended Application** \\ \hline \multirow{2}{*}{ResNet-50 (transfer)} & \(\bullet\) & Fast convergence due to residual blocks & \(\bullet\) & Large number of parameters \(\bullet\) & Classification of major lesion \\  & \(\bullet\) & Rich feature representations & datasets & & types \\ \hline \multirow{4}{*}{VGG-16} & \(\bullet\) & Simple, transparent architecture & \(\bullet\) & Very large model size & \(\bullet\) & Rapid prototyping and baseline comparisons \\  & \(\bullet\) & Well documented in literature & \(\bullet\) & Slow training & \\ \hline \multirow{4}{*}{GoogLeNet (transfer learning)} & \(\bullet\) & Well-established CNN with efficient Inception modules & \(\bullet\) & Complex multibranch design & \(\bullet\) & Multiclass skin lesion classification using \\  & \(\bullet\) & Effective when used with transfer learning and augmentation & \(\bullet\) & Outperformed by newer, deeper models & & pretrained weights (ISIC \\ \hline \multirow{2}{*}{DenseNet-121} & \(\bullet\) & Efficient gradient flow & \(\bullet\) & \multirow{2}{*}{Slower training than ResNet} & \(\bullet\) & Melanoma classification (transfer learning) \\  & \(\bullet\) & Reduced overfitting risk & \(\bullet\) & More complex hyperparameter tuning & \(\bullet\) & GPU-limited applications \\ \hline \multirow{4}{*}{EfficientNet-B0} & \(\bullet\) & Optimal balance of parameters and accuracy & \(\bullet\) & Specialized architecture for skin cancer (data rebalancing) & \(\bullet\) & Dermoscopic skin cancer diagnosis with imbalanced data \\  & \(\bullet\) & Automatic network scaling & \(\bullet\) & Increased training and inference complexity & \(\bullet\) & Ensemble-based skin lesion classification for maximum accuracy \\ \hline \multirow{4}{*}{DSCC\_Net} & \(\bullet\) & Very high AUC achieved on dermoscopic images & \(\bullet\) & \multirow{2}{*}{\begin{tabular}{} \end{tabular} } & \multirow{2}{*}{\begin{tabular}{} \end{tabular} } \\  & Class-balancing strategy improves rare lesion detection & \(\bullet\) & & \multirow{2}{*}{\begin{tabular}{} \end{tabular} } \\ \hline \multirow{4}{*}{SkinNet (ensemble learning)} & \(\bullet\) & Improves accuracy by ensembling multiple CNNs & \(\bullet\) & Increased training and inference complexity & \(\bullet\) & Ensemble-based skin lesion classification for maximum accuracy \\  & \(\bullet\) & Leverages complementary features of lightweight models & \(\bullet\) & Requires coordination of multiple models & \(\bullet\) & \multirow{2}{*}{\begin{tabular}{} \end{tabular} } \\ \hline \multirow{4}{*}{Swin Transformer} & \(\bullet\) & High accuracy via hierarchical self-attention & \(\bullet\) & Memory- and computation-intensive & \(\bullet\) & Large-scale skin lesion image classification state-of-the-art model \\  & \(\bullet\) & Captures local and global image context effectively & \(\bullet\) & Requires large training & \(\bullet\) \\ \hline \multirow{4}{*}{Separable Vision Transformer} & \(\bullet\) & Lightweight ViT design optimised for dermatology & \(\bullet\) & Requires very large training datasets to generalise & \(\bullet\) & High-precision lesion classification across diverse image sets (with sufficient data) \\  & \(\bullet\) & Achieves state-of-the-art classification accuracy & \(\bullet\) & Still computationally demanding (albeit optimised) & \(\bullet\) & \multirow{2}{*}{\begin{tabular}{} \end{tabular} } \\ \hline \multirow{2}{*}{UNet} & \(\bullet\) & Excellent segmentation precision & \(\bullet\) & Requires detailed pixel-level annotation & \(\bullet\) & Boundary segmentation of skin lesions \\  & \(\bullet\) & Easy to implement & & & \\ \hline \multirow{4}{*}{Attention UNet} & \(\bullet\) & Better focus on critical image regions & \(\bullet\) & \multirow{2}{*}{\begin{tabular}{} \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{} \end{tabular} } \\  & \(\bullet\) & Improved segmentation quality & \(\bullet\) & & \multirow{2}{*}{
\begin{tabular}{} \end{tabular} } \\ \hline \multirow{4}{*}{StyleGAN2 (augmentation)} & \(\bullet\) & Generates highly realistic synthetic images & \(\bullet\) & Difficult to stabilize training & \(\bullet\) & Augmentation of rare lesion types; training on small datasets \\  & \(\bullet\) & Helps to balance class distribution & \(\bullet\) & Possible artefact generation & \(\bullet\) & datasets \\ \hline \hline \end{tabular}
\end{table}