Research Article Multi-Class Skin Problem Classification Using Deep Generative Adversarial Network (DGAN) Maleika Heenaye-Mamode Khan , 1 Nuzhah Gooda Sahib-Kaudeer , 1 Motean Dayalen , 2 Faadil Mahomedaly , 2 Ganesh R. Sinha , 3 Kapil Kumar Nagwanshi , 4 and Amelia Taylor 5 1 Department of Software and Information Systems, University of Mauritius, Reduit, Mauritius 2 Accenture Technology, Ebene Cyber, Mauritius 3 Department of Electronics and Communication Engineering, Myanmar Institute of Information Technology, Mandalay, Myanmar 4 Department of Computer Science and Engineering, Amity University Rajasthan, Jaipur, Rajasthan 302006, India 5 Malawi University of Business and Applied Sciences, Blantyre, Malawi Correspondence should be addressed to Ganesh R. Sinha; drgrsinha@ieee.org and Kapil Kumar Nagwanshi; dr.kapil@ieee.org Received 22 December 2021; Accepted 3 March 2022; Published 24 March 2022 Academic Editor: Andrea Loddo Copyright © 2022 Maleika Heenaye-Mamode Khan et al. is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. e lack of annotated datasets makes the automatic detection of skin problems very difficult, which is also the case for most other medical applications. e outstanding results achieved by deep learning techniques in developing such applications have im- proved the diagnostic accuracy. Nevertheless, the performance of these models is heavily dependent on the volume of labelled data used for training, which is unfortunately not available. To address this problem, traditional data augmentation is usually adopted. Recently, the emergence of a generative adversarial network (GAN) seems a more plausible solution, where synthetic images are generated. In this work, we have developed a deep generative adversarial network (DGAN) multi-class classifier, which can generate skin problem images by learning the true data distribution from the available images. Unlike the usual two-class classifier, we have developed a multi-class solution, and to address the class-imbalanced dataset, we have taken images from different datasets available online. One main challenge faced during our development is mainly to improve the stability of the DGAN model during the training phase. To analyse the performance of GAN, we have developed two CNN models in parallel based on the architecture of ResNet50 and VGG16 by augmenting the training datasets using the traditional rotation, flipping, and scaling methods. We have used both labelled and unlabelled data for testing to test the models. DGAN has outperformed the conventional data augmentation by achieving a performance of 91.1% for the unlabelled dataset and 92.3% for the labelled dataset. On the contrary, CNN models with data augmentation have achieved a performance of up to 70.8% for the unlabelled dataset. e outcome of our DGAN confirms the ability of the model to learn from unlabelled datasets and yet produce a good diagnosis result. 1. Introduction According to Tizek et al. [1], one of the most common health problems affecting people of all ages worldwide is skin diseases. Acknowledging that many of these diseases can be treated nowadays, the burden of skin problems is still sig- nificant. A Bulletin of the World Health Organization in 2005 states that skin diseases have a significant impact on people’s quality of life, causing lost productivity at work and school and discrimination due to disfigurement [2]. Given the rise in the prevalence, seriousness, and consequence of skin diseases worldwide, studies and research are carried out periodically to help analyse and reveal the patterns, twists, and statistics about these diseases. e global burden of skin diseases is analysed in a study carried out by Coffeng et al. [3]. is study shows that skin diseases are responsible for 1.79% of the global burden of disease, measured in disability- adjusted life years (DALYs), that is, the number of years lost Hindawi Computational Intelligence and Neuroscience Volume 2022, Article ID 1797471, 13 pages https://doi.org/10.1155/2022/1797471