Table 13 Comparative analysis with state-of-the-art methods.
From: Advancing skin cancer diagnosis with deep learning and attention mechanisms
Reference | Model Name | Accuracy (%) |
|---|---|---|
7 Kaur et al. (2025) | Advanced Deep Learning Models for Melanoma Diagnosis | 90.87% |
9 Naeem et al. (2024) | SNC_Net: Handcrafted and Deep Learning-Based Features | 94.3% |
11 Wu et al. (2024) | MHorUNet | 95.6% |
12 Wu et al. (2024) | HSH-UNet | 96.2% |
16 Li et al. 2024 | DSEUNet | 95.1% |
10 Kandhro et al., 2024 | Performance Evaluation of E-VGG19 | 94.5% |
17 Ahamed et al., 2024 | UNet with Attention | 94.8% |
Proposed Model | SDAM with Enhanced UNet Architecture | 97.8% |
