Human Nail disease Diagnosis Using Machine Learning
K.Muthulakshmi
, K.Valarmathi , Priyadharshini E , Pavitraa D.S.B
Nail, color analysis, weka, classifier, image processing
In order to diagnose many disorders early on, human hand nails are examined. In the healthcare industry, a person's hand nail colour can be studied to identify a certain condition. The suggested system provides guidance for making a diagnosis of the disease in such a situation. Person nail image is the input for the suggested system. In order to diagnose diseases, the system will process the quotation image and extract properties from it. The suggested approach leverages variations in nail colour to identify diseases among the many characteristics of the human nail. Here, the Weka tool is used to build the first training set of data from photographs of the nails of patients with particular disorders. The outcome are obtained by comparing the features from the input nail images with the training data. .In this study, using the color properties of nail images, we found that the results of our evaluation reached an average of 65% data in education.
"Human Nail disease Diagnosis Using Machine Learning", TIJER - TIJER - INTERNATIONAL RESEARCH JOURNAL (www.TIJER.org), ISSN:2349-9249, Vol.11, Issue 2, page no.48-52, February-2024, Available :https://tijer.org/TIJER/papers/TIJERB001007.pdf
Volume 11
Issue 2,
February-2024
Pages : 48-52
Paper Reg. ID: TIJER_151155
Published Paper Id: TIJERB001007
Downloads: 000277
Research Area: Science and Technology
Country: TIRUVALLUR, TAMILNADU, India
ISSN: 2349-9249 | IMPACT FACTOR: 8.57 Calculated By Google Scholar | ESTD YEAR: 2014
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.57 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: TIJER (IJ Publication) Janvi Wave
