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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:IJSREM49586

Multi-Class Disease Detection and Wound Detection with CNN

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM49586

Volume:

9 |

6 |

Jun 2025

Pages:

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/ijsrem49586

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

K

Kavyashree.L

WJH

Waleed Javaid Hakak

YS

Yashas S

SS

Shamun Shaik

SM

Samarth Mishra

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Abstract

– This project focuses on the implementation of a deep Learning model capable of detecting skin diseases and Identifying wounds from images. By utilizing Convolutional Neural Networks (CNNs) and transfer learning, the system Classifies various skin conditions such as eczema, psoriasis, acne, and different types of wounds. The application aims to Provide preliminary diagnostics to aid dermatologists and Rural healthcare systems

KEYWORDS

Deep Learning Convolutional Neural Networks (CNNs) Transfer Learning Skin Disease Detection Wound Identification Image Classification Eczema Psoriasis Acne Wounds Medical Imaging Preliminary Diagnostics Dermatology Rural Healthcare

Cite This Article

Kavyashree.L, Waleed Javaid Hakak, Yashas S, Shamun Shaik, Samarth Mishra, "Multi-Class Disease Detection and Wound Detection with CNN," International Journal of Scientific Research in Engineering and Management, vol. 9, no. 6, Jun 2025. https://doi.org/10.55041/ijsrem49586

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Publication Date

6/5/2025

Peer Review Type

Double-Blind Peer Review

Review Rounds: 2

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Conflict of Interest

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

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