Exploring transfer learning to classify rare skin diseases

Paper Title:

Meta-Transfer Derm-Diagnosis: Exploring Few-Shot Learning and Transfer Learning for Skin Disease Classification in Long-Tail Distribution

Published on:

25 April 2024

Primary Category:

Computer Vision and Pattern Recognition

Paper Authors:

Zeynep Özdemir,

Hacer Yalim Keles,

Ömer Özgür Tanrıöver

Bullets

Key Details

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Examines episodic vs traditional training for rare skin disease classification

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Tests models using DenseNet121, MobileNetV2, ImageNet pretraining

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Transfer learning represents features well and improves performance

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Traditional training surpasses episodic as shots increase

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Data augmentation further boosts accuracy

Explore the topics in this paper

convolutional neural networks

few-shot learning

limited data

skin disease classification

transfer learning

AI generated summary

Exploring transfer learning to classify rare skin diseases

This paper examines combining few-shot learning and transfer learning to address challenges in classifying rare skin diseases with limited data. Models using DenseNet121, MobileNetV2, ImageNet pretraining, episodic training, and data augmentation are tested on datasets like SD-198. Key findings show transfer learning helps represent features and boosts performance, traditional training surpasses episodic past few shots, and data augmentation further improves accuracy, achieving state-of-the-art on some datasets.