ArticlePeerJ. Computer science2023
Classification of melanonychia, Beau's lines, and nail clubbing based on nail images and transfer learning techniques.
Article in PeerJ. Computer science, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Human expertise or artificial intelligence? A prospective study on nail disorder diagnosis.NPJ digital medicine · 2026Article
- Enhancing nail disease diagnosis: a capsule network with SE attention and dual backbone models.Naunyn-Schmiedeberg's archives of pharmacology · 2026Article
- Artificial Intelligence Assisted Smartphone System for Real-Time Detection and Severity Assessment of Digital Clubbing.International journal of medical sciences · 2026Article
- Autonomous detection of nail disorders using a hybrid capsule CNN: a novel deep learning approach for early diagnosis.BMC medical informatics and decision making · 2024Article
- Artificial Intelligence in Diagnosis and Management of Nail Disorders: A Narrative Review.Indian dermatology online journalArticle
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Authors and funding
2 authors.
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Abstract
Background: Nail diseases are malformations that appear on the nail plate and are classified according to their own signs and symptoms that may be related to other medical conditions. Although most nail diseases have distinct symptoms, making a differential diagnosis of nail problems can be challenging for medical experts. Method: One early diagnosis method for any dermatological disease is designing an image analysis system based on artificial intelligence (AI) techniques. This article implemented a novel model using a publicly available nail disease dataset to determine the occurrence of three common types of nail diseases. Two classification models based on transfer learning using visual geometry group (VGGNet) were utilized to detect and classify nail diseases from images. Result and Finding: The experimental design results showed good accuracy: VGG16 had a score of 94% accuracy and VGG19 had a 93% accuracy rate. These findings suggest that computer-aided diagnostic systems based on transfer learning can be used to identify multiple-lesion nail diseases.
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