## 5 Conclusions  
In this research, we presented a novel multi-input CNN model tailored for the classification of skin lesions, combining the strengths of advanced image processing and metadata analysis. The architecture leverages medical-specific augmentation techniques, such as controlled adjustments to image brightness and color, ensuring the preservation of critical diagnostic information while enhancing model robustness. Through the integration of multiple CNN architectures, including EfficientNetV2, ResNet-152, MobileNetV2, and ResNet-50, our approach captures a comprehensive range of features from dermatoscopic images. Additionally, the incorporation of Principal Component Analysis (PCA) for tabular data effectively reduces dimensionality, focusing the model's learning on the most salient features and thereby improving efficiency. Our method demonstrated superior performance on the test set, outperforming existing models in terms of accuracy, recall, precision, and F1 scores. These results highlight the efficacy of our integrated approach in handling the complexity of skin lesion diagnostics, providing an effective tool for improving diagnostic accuracy in clinical settings. The use of multiple inputs allows our model to leverage both visual and contextual data, offering a more holistic analysis and reducing the likelihood of misdiagnosis. While our model demonstrates substantial improvements in skin lesion classification, it does require significant computational resources, which could limit its deployment in settings with restricted technological infrastructure. Addressing these limitations, future work will explore the potential for simplifying the model's architecture without compromising its accuracy, aiming to make it more accessible and practical for use in varied clinical environments. Future work with a focus on implementing XAI techniques would allow us to provide clear insights into the decision-making processes of our model, increasing trust and understanding among clinicians. This transparency is crucial for clinical acceptance and could help in identifying potential biases or errors in AI's reasoning. Further research into advanced image processing techniques, such as super-resolution and noise reduction, could help in improving the quality of the input images. Enhanced image clarity might lead to better model performance, particularly in identifying subtle features of early-stage lesions. Also, developing more lightweight models that maintain high accuracy while being deployable on mobile devices or in remote areas could significantly expand the accessibility of advanced diagnostic tools. This is especially important in regions with limited medical infrastructure, where such tools can provide critical support to healthcare providers. This research was designed and written by R.M. ; conceptualization, R.M. and D.M. ; methodology, D.M. and R.M. ; software, D.M. ; writing--original draft preparation, D.M. ; supervision and other contributions, Y.-I.C. All authors have read and agreed to the published version of the manuscript. This paper is supported by Korean Agency for Technology and Standard under Ministry of Trade, Industry and Energy in 2023, project number is 1415181638 and the title is \"Establishment of standardization basis for BCI and AI Interoperability\", project number is 1415180835 and the title is \"Development of International Standard Technologies based on AI Learning and Inference Technologies\", and by the Gachon University Research Fund 2023 (GCU-202300770001). Not applicable. **Data Availability Statement**: Publicly available datasets were analyzed in this study. This data can be found here: [[https://www.kaggle.com/datasets/andrewmvd/isic-2019](https://www.kaggle.com/datasets/andrewmvd/isic-2019)]([https://www.kaggle.com/datasets/andrewmvd/isic-2019](https://www.kaggle.com/datasets/andrewmvd/isic-2019)) (accessed on 7 May 2024).