A Study on Deep Learning Methods for Skin Disease Classification

International Journal of Engineering and Management Research

View Publication Info
 
 
Field Value
 
Title A Study on Deep Learning Methods for Skin Disease Classification
 
Creator N.Vanitha
M.Geetha
 
Subject Disease of the Skin
Deep Learning
Types
Significance
 
Description Dermatological disorders are one among the foremost widespread diseases within the world. Despite being common its diagnosis is extremely difficult due to its complexities of skin tone, color, presence of hair. This paper provides an approach to use various computer vision-based techniques (deep learning) to automatically predict the varied sorts of skin diseases. The system makes use of deep learning technology to coach itself with the varied skin images. the most objective of this technique is to realize maximum accuracy of disease of the skin prediction. The people health quite the other diseases. Skin diseases are mostly caused by mycosis, bacteria, allergy, or viruses, etc. The lasers advancement and Photonics based medical technology is employed in diagnosis of the skin diseases quickly and accurately. The medical equipment for such diagnosis is restricted and costliest. So, Deep learning techniques helps in detection of disease of the skin at an initial stage. The feature extraction plays a key role in classification of skin diseases. The usage of Deep Learning algorithms has reduced the necessity for human labor, like manual feature extraction and data reconstruction for classification purpose.
 
Publisher Vandana Publications
 
Date 2021-04-30
 
Type info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Peer-reviewed Article
 
Format application/pdf
 
Identifier https://www.ijemr.net/ojs/index.php/ojs/article/view/743
10.31033/ijemr.11.2.7
 
Source International Journal of Engineering and Management Research; Vol. 11 No. 2 (2021): April Issue; 48-52
2250-0758
2394-6962
 
Language eng
 
Relation https://www.ijemr.net/ojs/index.php/ojs/article/view/743/833
 
Rights Copyright (c) 2021 International Journal of Engineering and Management Research
https://creativecommons.org/licenses/by-nc-nd/4.0
 

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