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Nail health can be an important diagnostic window into an individual's overall health, providing valuable information regarding the early expression of a wide range of systemic conditions including diabetes, cardiovascular disease, vitamin deficiencies, and fungal infections. Seeing the diagnostic capability of nail appearance, the proposed system nail insight brings forth a new and smart technique that leverages the strength of image processing and deep learning in detecting diseases early through nail analysis.The primary aim of nail insight is to offer a non-invasive, accessible, and precise method for detecting abnormalities through analyzing nail images. The system starts off with acquiring high-resolution nail images of the user through generic digital cameras or cell phones. These pictures are then passed through a strong preprocessing pipeline that involves noise removal, contrast adjustment, and edge extraction. These pre-processing operations are critical in making sure the images are clean, feature-full, and ready for subsequent analysis.Now that the images are pre-processed, sophisticated segmentation techniques are used to extract the region of the nail from the surrounding skin and background
Keywords:
deep learning,covolutional neuaral network,early disease detection,camera,nail abnormalities
Cite Article:
"NAIL IMAGE ANALYSIS FOR EARLY DISEASE DETECTION", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 5, page no.b1-b5, May-2025, Available :http://www.ijrti.org/papers/IJRTI2505101.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