What question did this study set out to answer?

This research aims to create an accurate and interpretable deep learning model for skin cancer detection.

February 19, 2026Neural Computing and Applications

A comparative analysis for skin cancer detection by using explainable deep learning

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This research aims to create an accurate and interpretable deep learning model for skin cancer detection.
Utilized the HAM10000 dataset for training deep learning models.
Trained MobileNet, DenseNet, and XceptionNet architectures.
Employed data augmentation to address class imbalance.
Evaluated model performance using accuracy, precision, recall, F1-score, and AUC metrics.
Integrated explainable AI techniques like Grad-CAM and LIME.
MobileNet achieved the highest classification accuracy at 84.04%.
DenseNet followed with 83.57% accuracy and XceptionNet at 81.52%.
XAI methods significantly enhanced model interpretability.
The study illustrated that XAI increases reliability and clinical utility of models.

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Implication

This analysis demonstrates the effectiveness of deep learning methods for skin cancer detection, indicating improved model interpretability through XAI techniques.

Cite This Study

ARAS et al. (2026) studied this question.

synapsesocial.com/papers/6996712d80e1323b05ec049f https://doi.org/10.1007/s00521-025-11809-y

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Implication

This analysis demonstrates the effectiveness of deep learning methods for skin cancer detection, indicating improved model interpretability through XAI techniques.

A comparative analysis for skin cancer detection by using explainable deep learning

Key Points

Discussion

Implication

Cite This Study

A comparative analysis for skin cancer detection by using explainable deep learning

Key Points

Discussion

Implication

Cite This Study

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