A hybrid model for improved nail disease classification using vision transformers and stable diffusion.

Rahul Nijhawan, Ananya Gupta, Manoj Diwakar, Prabhishek Singh, Anchit Bijalwan

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Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 authors.

Rahul NijhawanThapar Institute of Engineering and Technology, Patiala, Punjab, India.

Ananya GuptaThapar Institute of Engineering and Technology, Patiala, Punjab, India.

Manoj DiwakarGraphic Era Deemed to be University, Dehradun, Uttrakhand, India.

Prabhishek SinghSchool of Computer Science Engineering and Technology, Bennett University, Greater, Noida, India.

Anchit BijalwanFaculty of Electrical and Computer Engineering, Arba Minch University, Arba Minch, Ethiopia. anchit.bijalwan@gmail.com.

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6 · The paper itself

Abstract

Nail diseases, including various fungal infections, paronychia, and psoriasis, affect millions, and their treatment relies on visual inspection, which may result in inaccurate and delayed treatments. This study explores the use of synthetic nail disease data generated through stable diffusion models to increase the accuracy of the machine learning model. We aim to incorporate enhanced data transformation techniques by utilizing a few-shot learning technique with a small amount of data representation in the text-to-image stable diffusion model. This model renders synthetic data, which provides variety and diversity while preserving the critical components of the original data. The experimental results display the efficacy of the synthetic data that helps provide robustness to the pre-trained CNN MobileNetV2 and Vision Transformer model on a custom real-world nail disease dataset, providing an increased accuracy of 3.26% for MobileNetV2 and 3.02% for Vision Transformers.

Indexed as

Machine LearningNail DiseasesConvolutional Neural NetworksHumansData augmentationNail diseaseNail disease datasetStable diffusionText-to-imageTransfer learning

Identifiers

PMID41526384

DOI10.1038/s41598-025-04538-8

PMCPMC12800211

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LicenceCC BY-NC-ND

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