EfficientNet-Based Melanoma Classification with CBAM Attention and Monte Carlo Dropout for Robust Uncertainty Estimation

Recent developments in deep learning have demonstrated tremendous potential for enhancing medical picture classification tasks, particularly for the detection of skin malignancies like melanoma. However, it is still a huge challenge to guarantee high accuracy, reliability, and interpretability in real clinical settings. This study attempted to resolve these issues by proposing a novel approach to melanoma detection, by employing diverse techniques such as the Convolutional Block Attention Module (CBAM), binary focal loss, and Monte Carlo Dropout (MC Dropout) for uncertainty estimation. The CBAM attention module was inserted to help the network focus on important features of images, and focal loss was applied to solve class imbalance and encourage learning from hard samples. MC Dropout was used to achieve an uncertainty estimate in the test set, and thus, more reliable and interpretable predictions. The approach was implemented with a pre-trained deep CNN called EfficientNetB4 as the backbone and trained on a large melanoma dataset, which is separated into training sets, test sets, and validation sets in order to test the performance. Model evaluation was performed using accuracy, precision, recall, F1-score, and AUC, resulting in 0.95 for accuracy, whereas the AUC value is 0.98. Furthermore, the uncertainty estimate made a clearer decision-making, and the interpretability was crucial when used as a clinical task model. These results highlight the necessity to combine attention mechanisms, task-specific loss terms, and uncertainty quantification for building accurate and interpretable AI in medical domains. The study prototype has the potential for improving the detection of early-stage melanoma and provides useful guidance to future AI-based healthcare services.

@article{Voggu2025,
  title     = {EfficientNet-Based Melanoma Classification with CBAM Attention and Monte Carlo Dropout for Robust Uncertainty Estimation},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {12},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Soujenya Voggu and Shadab Siddiqui and Shahin Fatima},
  doi       = {10.14569/IJACSA.2025.0161249},
  url       = {https://doi.org/10.14569/IJACSA.2025.0161249}
}