Bioinformatics-driven deep learning for nail disease diagnosis: A novel approach to improve healthcare outcomes

In order to increase awareness of the importance of nail care in preventing disease and enhancing quality of life, this study investigates the use of convolutional neural networks, or CNNs. Onychomycosis and other nail disorders are quite prevalent worldwide and are associated with inadequate personal cleanliness. The study used a dataset of 655 nail photos that had been pre-processed to 224x224 pixel resolution and categorized into 17categories. The CNN model performed well in identifying illnesses like “Leukonychia,” achieving an overall accuracy of 83%; however, it needs to be improved for underrepresented classifications like “Pale Nail.” The study recommends data augmentation, model parameter optimization, and dataset expansion to improve accuracy. To confirm dependability in practical contexts, testing with clinical datasets is also advised. A user-friendly interface for wider accessibility is one of the future aims, which will allow for prompt and precise preliminary diagnosis. This study shows how CNN-based technologies can be used to quickly and easily identify nail disorders, improving access to treatment and preventing disease