Identification of nail diseases using DenseNet169 with leaky ReLU and LSTM with data balancing method
Year: 2025
Type: article
Abstract: 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.
Source: Discover Computing
Authors: Kamini G. Panchbhai, Madhusudan G. Lanjewar, Panem Charanarur, Sandipkumar Agrawal
Institutions: Goa University, National Forensic Sciences University
Language: English
Topic: Nail Diseases and Treatments
Subfield: Dermatology
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $1,520
OpenAlex ID: W4416454635