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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:IJSREM26651

Skin Disease Detection Using Convolutional Neural Network

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM26651

Volume:

7 |

11 |

Nov 2023

Pages:

Publication Frequency:

Monthly (12 issues per year)

DOI
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Not assigned

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

DLD

Dr. Latika Desai

SB

Sakshi Bhosale

SC

Sonam Chavan

AG

Akshay Gaikwad

IJ

Ishika Jadhav

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Abstract

Skin is the like powerful protection of important organs in the human body. It acts as a shield to protect our internal body to get damaged. But this important part of the human body can be affected by serious infections caused by some fungus or viruses or even dust too. Due to acne problems to eczema people suffer a lot. A proper diagnosis can result in proper medication that can degrade the miseries of the people suffering. The goal of this system is to develop a prototype to detect skin diseases using neural networks. In the choice of neural networks, we have chosen CNN (Convolutional Neural Network). In this application picture handling strategies are used. Users have to take a photo of the contaminated region of their skin and transfer it to the system. The transferred pictures of illness will be handled in the focal server and it will answer with the disease name. Convolutional neural network (CNN) have been applied in this study for the identification of skin diseases.

KEYWORDS

Disease detection CNN (Convolutional Neural Network)

Cite This Article

Dr. Latika Desai, Sakshi Bhosale, Sonam Chavan, Akshay Gaikwad, Ishika Jadhav, "Skin Disease Detection Using Convolutional Neural Network," International Journal of Scientific Research in Engineering and Management, vol. 7, no. 11, Nov 2023.

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Publication Date

11/10/2023

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Double-Blind Peer Review

Review Rounds: 2

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The authors declare no conflict of interest related to this research work.

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