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<p>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.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月21日

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  • Kamini G. Panchbhai,
  • Madhusudan G. Lanjewar,
  • Panem Charanarur,
  • Sandipkumar Agrawal

摘要

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.