Classification of Six Nail Conditions Using Deep Learning

Nail diseases can be classified by the nail's abnormal shape, texture, color, form, and thickness. Traditionally, nail diseases were classified using manual visual inspections or laboratory. However, recent advancements focus more on using image processing for diagnosis. This study employs the DenseNet-121 deep learning model to classify six nail conditions—Beau's line, Melanonychia, Nail clubbing, Onycholysis, Terry's nail, and normal nails—using 750 images from Kaggle. The dataset was split into training (76%), validation (14%), and testing (10%). After 61 epochs, the model achieved 84.6% accuracy, with 85% precision and an F1 score of 84%. While these results indicate the potential of this model in aiding the diagnosis, further research with larger and more diverse datasets is necessary to improve model performance.