# Exploring Pre-Trained Models for Skin Cancer Classification
## 4 Results and Discussions
Figures 8 and 9 demonstrate the application of the integrated gradients technique to provide interpretability in the model's predictions for benign and malignant skin lesions. In Figure 8, which presents benign samples, the original images show lesions with relatively uniform color distribution and well-defined borders. The integrated gradients heatmaps reveal sparse and less intense red regions, indicating that the model primarily focuses on specific, localized features without complex patterns. The overlay images further confirm that these highlighted areas are concentrated and less irregular, consistent with the characteristic appearance of benign lesions. In contrast, Figure 9 presents malignant samples, which exhibit irregular borders, heterogeneous pigmentation, and more complex structures. The corresponding integrated gradients heatmaps display more extensive and intense red regions, suggesting that the model identifies intricate patterns and variations in pigmentation commonly associated with malignancy. The overlay images show broader and more dispersed areas of interest, particularly around irregular and asymmetric patterns. Figure 7: Prediction results of the proposed models. Figure 8: IG technique applied to ResNet50 model for benign sample. Figure 9: IG technique applied to ResNet50 model for malignant sample. A comparison of Figures 8 and 9 indicates that the model activation is more pronounced and widespread in malignant lesions compared to benign lesions, reflecting the distinct morphological differences between these two categories. This analysis highlights the effectiveness of the integrated gradients technique in enhancing the transparency and interpretability of the model's decision-making process for skin lesion classification. Figures 10 and 11 demonstrate the application of the LIME (Local Interpretable Model-agnostic Explanations) technique to provide interpretability for the model's predictions of benign and malignant skin lesions. In Figure 10, which presents benign samples, the original images show lesions with relatively uniform pigmentation and well-defined borders. The LIME, highlighted with yellow outlines, indicates the regions that contributed most significantly to the model's prediction. These highlighted areas are primarily localized and concentrated around distinct lesion boundaries, suggesting that the model focuses on consistent and uniform features typically associated with benign lesions. In contrast, Figure 11 illustrates malignant samples, which are characterized by irregular borders, heterogeneous pigmentation, and structural asymmetry. The LIME for these samples shows more dispersed and irregularly shaped highlighted regions. The yellow  
Figure 11: LIME technique applied to ResNet50 model for malignant sample. Figure 10: LIME technique applied to ResNet50 model for benign sample. outlines often encompass multiple areas within the lesion, indicating that the model identifies complex patterns such as uneven pigmentation, irregular borders, and asymmetric structures as significant for malignancy. A comparison between Figures 10 and 11 reveals a distinct difference in the model's behavior when predicting benign versus malignant lesions. The benign samples exhibit more compact and localized areas of interest, whereas the malignant samples display more extensive and fragmented highlighted regions. This pattern aligns with the clinical characteristics of these lesion types, demonstrating the effectiveness of the LIME technique in providing insights into the model's decision-making process for skin lesion classification. When comparing the integrated gradients and LIME techniques, notable differences emerge in the model's interpretability patterns. Integrated gradients highlight pixel-level attributions with a focus on continuous color and texture variations within the lesion, which is useful for identifying subtle differences in structure. In contrast, LIME provides a more segmented explanation by identifying larger, discrete areas that influence the model's prediction.