## 4 Implementation
### Comparison with Related Methods  
In our study, we conducted a detailed comparison between our multi-input CNN model and other related methods, primarily focusing on key performance metrics such as accuracy, recall, precision, and F1 scores. This comparative analysis was essential to evaluate the efficacy of our approach in the context of existing methodologies and to highlight the advancements our model introduces in the field of skin lesion classification. Our multi-input CNN model demonstrated higher performance across several metrics when compared to the benchmark models. Notably, the integration of image and tabular data in our model allowed for a more nuanced analysis, leading to higher accuracy and F1 scores. The specific adjustments and optimizations in our model architecture, such as the strategic use of different CNN backbones and the tailored handling of tabular data through PCA and MLP, were key factors in achieving these results. The success of our approach suggests promising directions for further research and potential implementation in clinical environments, where accurate and reliable diagnoses can significantly impact patient outcomes. Table 6 shows the results from several existing research papers and our proposed method in terms of accuracy, recall, precision, and F1 score.  
Figure 6: Confusion matrix for skin lesion classification model.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Source** & **Methodology** & **Accuracy** & **Recall** & **Precision** & **F1 Score** & **Advantages and Limitations** \\ \hline \multirow{9}{*}{[PERSON]. [PERSON] et al. [18]} & Incorporating multi-input streams and an attention mechanism to address challenges such as class similarity and foreground-background imbalances. The model integrates downsampling for global context, and targeted local views enhanced by CAM (Class Activation Mapping) to rectify imbalances, which are then processed through Dense Blocks for feature consolidation. & 88.4\% & 76.7\% & 96.3\% & N/A \\  & & & & & & \\  & & & & & & \\  & & & & & & \\  & & & & & & \\  & & & & & & \\  & & & & & & \\  & & & & & & \\  & & & & & & \\  & & & & & & \\ \hline \multirow{9}{*}{[PERSON] et al. [19]} & The model leveraging transfer learning with the pretrained GoogleNet architecture. & 94.92\% & 79.8\% & 97\% & 80.36\% & \
\begin{tabular}{c} The use of a pretrained GoogleNet in our model accelerates feature extraction and training, leveraging transfer learning for rapid, high-accuracy convergence. However, reliance on GoogleNet’s architecture may restrict adaptability to new or complex datasets, requiring significant adjustments for optimal performance. \\ \hline \multirow{9}{*}{[PERSON] et al. [20]} & Ensemble of deep learning models; addresses class imbalances and multiple input resolutions with a data-driven approach. & N/A & 71.7\% & N/A & N/A \\  & & & & & & \\  & & & & & & \\ \hline \multirow{9}{*}{[PERSON] et al. [20]} & A multi-faceted deep learning approach is employed for skin lesion classification, where each image is first segmented using R2U-Net to effectively isolate the lesion area. The segmented images are then processed using a suite of convolutional neural networks including EfficientNetB0-B7, SENet-154, ResNeXt-101 32x4d, and Inception-ResNet-v2 to enhance feature extraction and classification accuracy. & 96.86\% & 85\% & 82\% & 83\% &
\begin{tabular}{c} The methodology benefits from comprehensive segmentation and the use of on ensemble of powerful CNN architectures, enhancing the model’s ability to handle diverse and complex image characteristics. Method did not use metadata. \\ \end{tabular} \\  & & &