Exploiting Transformer Network for Nail Diseases Classification and Recognition
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Abstract
Diagnosing nail diseases is a complex task due to their similar visual characteristics, often requiring expert dermatologists for accurate assessment. Misdiagnosis can lead to ineffective treatment and prolonged patient discomfort. This study explores the use of a transformer neural network for classifying nail diseases, leveraging its ability to identify intricate patterns and subtle features that may indicate early signs of disease. The research focuses on three nail conditions: psoriasis nails, onychomycosis, and healthy. The model was trained with a carefully optimized set of hyperparameters to improve learning efficiency and classification performance. Experimental results showed that the network achieved a peak accuracy of 99.40%, demonstrating its ability to effectively distinguish between different nail conditions. This approach not only enhances classification accuracy but also has the potential to reduce the workload of healthcare professionals and speed up diagnosis. Ultimately, this advancement could contribute to the development of automated diagnostic systems, leading to improved patient care and treatment outcomes.
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