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Skin diseases, including common conditions like acne, eczema, and more severe disorders such as melanoma, represent a significant global health concern. Early and accurate detection is crucial for preventing complications and improving patient outcomes. Traditional diagnostic methods often rely on visual examination by dermatologists, which can be time-consuming and error-prone due to the high variability in skin lesions.
Recent advancements in deep learning, particularly Convolutional Neural Networks (CNNs), have demonstrated remarkable performance in medical image analysis. However, training CNNs from scratch requires large annotated datasets and substantial computational resources. Transfer learning addresses these challenges by utilizing pre-trained models on large, generic datasets, which can be fine-tuned to specific tasks with a smaller, specialized dataset. This significantly improves performance and reduces training time, making it a promising approach for skin disease detection.
Keywords:
CNN (convolutional layer network), transfer learning, pretrained model, deep learning.
Cite Article:
"Skin disease detection system using CNN", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 5, page no.c240-c244, May-2025, Available :http://www.ijrti.org/papers/IJRTI2505225.pdf
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ISSN:
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator