## 4 Discussion
Despite a slightly lower recall (0.8124), the method achieves the highest accuracy (0.9533) among all models compared, indicating excellent overall classification reliability. These results demonstrate that our approach provides a more balanced trade-off between minimizing false alarms and maintaining high classification performance. Future work will focus on optimizing the computational pipeline for real-time deployment, including GPU acceleration or applying dimensionality reduction techniques such as PCA or autoencoders to reduce the processing burden introduced by the Hermite transform, particularly when applied across multiple channels. While the results are promising, some limitations exist. The computational complexity of the Hermite transform, mainly when applied to multiple channels and high-resolution images, can be a challenge for real-time deployment. Future studies could explore optimizing this process or applying dimensionality reduction techniques to streamline computation. Testing the method on other datasets or clinical settings will also validate its generalizability and real-world applicability. This study presents a novel hybrid approach for automated skin lesion classification, combining the Hermite transform, Radial Fourier-Mellin transform, and Hilbert transform. Integrating these transforms across RGB and grayscale channels allows for comprehensive feature extraction, capturing both global invariance properties and localized spatial details. Experimental results demonstrate that this method enhances classification, accuracy, and robustness, outperforming traditional transform-based approaches. The findings underscore the potential of hybrid feature extraction techniques for advancing AI-driven dermatological diagnosis tools. Future work will focus on reducing \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Model** & **Dataset** & **Recall** & **FP Rate** & **Specificity** & **Precision** & **Accuracy** & **F1 Score** \\ \hline
2D superpixels + & & & & & & & \\ RCNN [28] & HAM-10000 & 0.8450 & - & - & 0.8349 & 0.8550 & 0.8530 \\ ResNeXt101 [29] & ISIC-2019 & 0.8810 & - & - & 0.8740 & 0.8850 & 0.8830 \\ MobileNetV2 [30] & ISIC-2019 & 0.8633 & - & - & 0.7890 & 0.8530 & - \\ VGG19 [31] & ISIC-2019, & & & & & & \\  & Derm-IS & & & & & & \\ ConvNet [32] & ISIC-2019, & & & & & & \\  & Derm-IS & & & & & & \\ Inception-v2 [33] & ISIC-2019 & 0.9015 & - & - & 0.8737 & 0.8904 & 0.8876 \\ This work & ISIC-2019 & 0.8124 & 0.0267 & 0.9733 & 0.8077 & 0.9533 & 0.8082 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of the proposed method with state-of-the-art models for skin lesion classification using the ISIC-2019 and other benchmark datasets. computational costs and expanding the model's validation to other datasets and clinical applications. Improved precision in automated skin lesion classification suggests that this method can contribute to non-invasive, early detection strategies, ultimately benefiting patient care and clinical decision-making. Methodology, L.F.L.-A. and J.A.-B. ; Software, L.F.L.-A. and J.A.-B. ; Validation, L.F.L.-A. and J.A.-B. ; Visualization, L.F.L.-A. and J.A.-B. ; Supervision, J.A.-B. ; Project administration, J.A.-B. ; Funding acquisition, J.A.-B. All authors have read and agreed to the published version of the manuscript. This research was funded by Centro de Investigacion Cientifica y de Educacion Superior de Ensenada, B. C. (CICESE), funding number is F0F181. Not applicable. **Data Availability Statement:** Available online: [[https://challenge.isic-archive.com/data/#2019](https://challenge.isic-archive.com/data/#2019)]([https://challenge.isic-archive.com/data/#2019](https://challenge.isic-archive.com/data/#2019)), accessed on 10 January 2019. [PERSON] in Centro de Investigacion Cientifica y de Educacion Superior de Ensenada, B. C. (CICESE) was supported by SECIHTI, with postdoc application number 4553917, CVU 693156, Clave: BP-PA-20230502163027674-4553917. The authors declare no conflicts of interest.