Improved Skin Disease Classification Using Generative Adversarial Network

Identifying skin diseases, such as leprosy, Tinea Versicolor, and Vitiligo identification is one of the challenging tasks. Therefore, skin disease identification success rate is comparatively poor as compared to the other computer vision tasks. Traditional Deep Learning (DL) models are not successfu...

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Bibliographic Details
Main Authors: Mondal, Bisakh, Das, Nibaran, Santosh, K.C., Nasipuri, Mita
Format: Conference Proceeding
Language:English
Subjects:
Data Augmentation
Data models
Deep Learning
Diseases
Gallium nitride
Generative Adversarial Network
Generative adversarial networks
Machine learning
Skin
Skin Disease
Training
Online Access:Request full text
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Summary:Identifying skin diseases, such as leprosy, Tinea Versicolor, and Vitiligo identification is one of the challenging tasks. Therefore, skin disease identification success rate is comparatively poor as compared to the other computer vision tasks. Traditional Deep Learning (DL) models are not successful in this domain due to the lack of a huge number of data. To address the problem, in the present work, we introduced a customized Generative Adversarial Network (GAN) to generate synthetic data. With data augmentation, we achieved maximum 94.25% recognition accuracy using DensenNet-121, which was 10.95% better than when no augmentation was employed. Source code is publicly available at https://github.com/DVLP-CMATERJU/SkinDiseases_GenerativeAI.git GitHub.
ISSN:2372-9198
DOI:10.1109/CBMS49503.2020.00104