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March 13, 2026Vietnam Journal of Computer ScienceOpen Access

Enhanced Vitiligo Classification Using Firefly-Optimized DenseNet201: A Comparative Study of State-of-the-Art Transfer Learning Models

Shardha Nand Usman Amjad Asif Raza Nancy Alias Shivani Kumari

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Key Points

This research aims to improve the classification of vitiligo using advanced deep learning models and optimization techniques.
Examined various deep learning models including MobileNet, ResNet50, and DenseNet201.
Developed firefly-optimized DenseNet201 for improved image classification.
Utilized a dataset of 3,628 skin images for training and validation purposes.
Firefly-optimized DenseNet201 achieved a high training accuracy of 97.77% and a validation accuracy of 89.58%.
MobileNet reached a training accuracy of 93.86% but overfitted after 4 epochs.
ResNet50 maintained a steady validation accuracy of 87.4%.

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Implication

Comparative study reveals improved vitiligo detection in medical images using firefly-optimized DenseNet201.

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Cite This Study

Nand et al. (2026) studied this question.

synapsesocial.com/papers/69b64d5cb42794e3e660e30c https://doi.org/10.1142/s2196888826500077

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