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January 1, 2026International Journal of Medical Engineering and Informatics

An efficient skin cancer detection and classification performance using adaptive residual DenseNet with attention mechanism

S. Jenita Christy G. Rosline Nesa Kumari

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Key Points

To improve the detection and classification performance of skin cancer using adaptive residual DenseNet with an attention mechanism.
Utilized adaptive residual DenseNet architecture for image analysis.
Implemented an attention mechanism to enhance feature extraction.
Evaluated the model's performance on skin cancer datasets.
Achieved higher classification accuracy compared to traditional methods.
Demonstrated improved detection rates for various skin cancer types.
Indicated reduced false positive and false negative rates.

Implication

Demonstrates improved detection and classification of skin cancer using advanced AI techniques, suggesting enhanced diagnostic tools.

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Cite This Study

Christy et al. (2026) studied this question.

synapsesocial.com/papers/69d5f09e74eaea4b11a79ff0 https://doi.org/10.1504/ijmei.2026.10077487

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Keep reading

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3ENDNet: A lightweight attention-enhanced feature fusion framework for multi-class skin cancer classificationOriginal Research · 2026
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5Skin Cancer Detection Approach Using Convolutional Neural Network Artificial Intelligence2024 · 4 citations

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