Advanced Recurrent Neural Networks for Nail Disease Detection: A Comparative Analysis

Nail disease, although frequently considered minor conditions, they are able to suggest more extreme underlying health problems and, if left untreated, can result in infection, pain, or maybe permanent nail damage. Conditions like brittle onychomycosis and psoriasis are frequently misdiagnosed because of their comparable look and minor signs and symptoms. To address this an automatic nail disease category model has been evolved the usage of advanced image evaluation techniques. To enhance feature extraction, a comparison examine was achieved among an Enhanced RNN that integrated VGG-16 layers and a Basic RNN that used Long Short-Term Memory (LSTM) units. Traditional methods of diagnosis are prone to errors and depend upon gradual guide tests, at the same time as the enhanced RNN model captures intricate spatial capabilities with VGG-based totally definitely convolutional layers followed via the use of LSTM gadgets that system sequential image patterns. The Enhanced RNN provides progressed accuracy over the Basic RNN, reaching 90% accuracy through the 20 epochs. This makes it an efficient and dependable tool for the early detection and classification of nail diseases.