Optimized Nail Disease Detection Using DenseNet121: Advancing Dermatological Diagnostics with Deep Learning

Nail disorders can be overt manifestations of many systemic diseases. Early diagnosis is always imperative in such cases. However, existing diagnostic methodologies are generally bounded by availability and subjective interpretation. Advanced deep-learning approaches have been proposed in this research work for the automated detection and classification of nail diseases. In this paper, we employed one of the strong convolutional neural networks, DenseNet121, optimized through transfer learning on a Kaggle dataset comprising 3835 images from six classes. Preprocessing at length was carried out systematically by resizing, normalizing, and enhancing through data augmentation to achieve uniform improvement in generalization. The results reflected an overall accuracy of 89%, with most classes having high precision and recall. These included the F1 scores and a confusion matrix, underlining its excellence in classifying well-represented categories such as Acral Lentiginous Melanoma and Clubbing, while it was worse for classes such as Onychogryphosis and Blue Finger. This model achieved a peak validation accuracy of 98.9%, with only minor overfitting, hence showing much promise for real-world application in further improving Dermatological AI with its robust framework for detecting nail diseases. The results point out the potential of deep learning in medical diagnostics while opening up ways toward accessible and scalable health solutions. Future work will increase the dataset, investigate deeper structures, and clinically integrate the model for better clinical diagnosis support.