Identification of nail diseases using DenseNet169 with leaky ReLU and LSTM with data balancing method

Nail diseases pose significant health concerns and often require prompt diagnosis and treatment. The authors propose a new approach for identifying nail diseases using advanced deep learning (DL) techniques. Specifically, we employ a modified DenseNet169 architecture, integrating Leaky Rectified Linear Unit (ReLU) activation and Long Short-Term Memory (LSTM) layers to extract features from nail images effectively. Our methodology involves pre-processing the images, training the modified DenseNet169-LSTM model, data balancing, and evaluating its performance using various metrics. The proposed method achieved an F1 score of 89.9%, while average Area Under the Curve of 98.2%, F1 score of 84.7%, Matthews correlation coefficient (MCC) of 84.7% and a Kappa score of 84.6%, with 95% confidence intervals (CI) of 83.7% (lower) and 87.3% (higher) and a p-value of 0.016. Moreover, the method’s robustness was also tested using the 5-fold method. The proposed approach demonstrates promising results in accurately identifying nail diseases, offering potential applications in clinical settings for timely diagnosis and treatment.