Classification of Skin Diseases Using Convolutional Neural Networks (VGG) with Histogram Equalization Preprocessing

Title

Classification of Skin Diseases Using Convolutional Neural Networks (VGG) with Histogram Equalization Preprocessing

Creator

Sai Saroja N.J.; Jacob L.

Description

Skin diseases are a major global health concern for which prompt and precise diagnosis is necessary for effective treatment. Convolutional neural networks (CNN), one of the deep learning techniques, have shown potential in automating the diagnostic procedure. The goal of this research is to enhance the effectiveness of skin disease categorization by fusing the capabilities of CNNs - particularly the VGG architecture - with the histogram equalization preprocessing method. In image processing, histogram equalization is a commonly used approach to enhance the contrast and general quality of medical photographs, which include photos of skin conditions. In order to improve the characteristics and details of dermatological pictures for this study, we employed histogram equalization as a preprocessing step. This allowed CNN to extract pertinent features more quickly. 2024 IEEE.

Source

TQCEBT 2024 - 2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies 2024

Date

2024-01-01

Publisher

Institute of Electrical and Electronics Engineers Inc.

Subject

Classification of images; Classification of skin diseases; Convolutional neural network (CNN); Histogram equalization; VGG

Coverage

Sai Saroja N.J., Christ University, Lavasa, India; Jacob L., Christ University, Lavasa, India

Rights

Restricted Access

Relation

ISBN: 979-835038427-7

Format

Online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1109/TQCEBT59414.2024.10545241

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85204495590&doi=10.1109%2fTQCEBT59414.2024.10545241&partnerID=40&md5=23ccf2ccd936bf7f93ddbc0d66f90e26