Deep Learning Approach for Diagnosing Papulosquamous Disease
Deep Learning Approach for Diagnosing Papulosquamous Disease |
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| © 2025 by IJETT Journal | ||
| Volume-73 Issue-3 |
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| Year of Publication : 2025 | ||
| Author : G. Nagarajan, Arun Raaza, Chetna Dayanand Achar, M. Meena |
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| DOI : 10.14445/22315381/IJETT-V73I3P105 | ||
How to Cite?
G. Nagarajan, Arun Raaza, Chetna Dayanand Achar, M. Meena, "Deep Learning Approach for Diagnosing Papulosquamous Disease," International Journal of Engineering Trends and Technology, vol. 73, no. 3, pp. 58-72, 2025. Crossref, https://doi.org/10.14445/22315381/IJETT-V73I3P105
Abstract
The six papulosquamous skin diseases, including psoriasis, psoriatic arthritis, eczema, and dermatitis, can be difficult to diagnose and treat due to their similar clinical manifestations. Diseases of the skin can be caused by a combination of environmental and genetic factors, and their consequences can be devastating. Developing early and automatic skin disease predictions is essential to reduce the dermatologist’s workload and enhance treatment outcomes. This study uses the Xiangya-Derma knowledge-based clinical image database in order to gain a greater understanding of skin diseases. A complex deep neural network is trained from the images in the dataset to extract the affected area adaptively. Numerous statistical characteristics are extracted and analyzed using the NDL method. The classifier uses a training procedure and a number of stacked “layers” to recognize skin disease indicators. The effectiveness of the Python-implemented system under discussion was evaluated using a variety of performance metrics. In this research, 70% of the data was used for training, 20% for testing, and 10% for validation.
Keywords
Papulosquamous, China’s largest clinical image dataset, Adaptive optimized convoluted deep neural network, Neuroevolution deep learning.
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