Classification of Vitiligo using CNN Autoencoder

Title

Classification of Vitiligo using CNN Autoencoder

Creator

Agrawal N.; Aurelia S.

Description

Precise recognition of skin ailment is a time-consuming procedure even for Professionals. With the invention of deep learning and medical image processing, the identification of skin disease is possible in a time-efficient manner and accurately. Autoencoder is the generative algorithm but in the proposed work it is used as a generator and as well as a classifier. In this work, a Convolutional (CNN) autoencoder was used to classify the skin disease Vitiligo. In this work encoding and decoding layers were used but in the last layer in place of reproducing the original image, the classification layer was used to classify the image. The proposed work gave training accuracy of 87.71 % whereas validation accuracy was 90.16%. 2022 IEEE.

Source

Proceedings - International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2022, pp. 174-176.

Date

2022-01-01

Publisher

Institute of Electrical and Electronics Engineers Inc.

Subject

Auto Encoder; Convolutional Neural Network; Deep Learning; Skin disease; Vitiligo

Coverage

Agrawal N., Christ Deemed to Be University, Department of Computer Science, Bengaluru, India; Aurelia S., Christ Deemed to Be University, Department of Computer Science, Bengaluru, India

Rights

Restricted Access

Relation

ISBN: 978-166549710-7

Format

Online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1109/ICAAIC53929.2022.9792650

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85133469629&doi=10.1109%2fICAAIC53929.2022.9792650&partnerID=40&md5=a68ad5de4e289757ddd0d7d0fe6bcd26