Automatic Detection of Systemic Diseases to Recognize Mpox Virus using GPLNet Based on Skin Lesions

• Published in: J.UCS (Annual print and CD-ROM archive ed.) Vol. 30; no. 10; pp. 1286 - 1315
• Main Authors: Bintoro, Panji, Zulkifli, Zulkifli, Heryadi, Yaya, Fitriana, Fitriana, Anggista Putri, Nopi, Yana Ayu Andini, Dwi
• Format: Journal Article
• Language: English
• Published: Bristol Pensoft Publishers 28-09-2024; Graz University of Technology
• Subjects: Accuracy; Algorithms; Analysis; Animal bites; Artificial neural networks; CNN; Deep learning; Development and progression; Disease; Disease transmission; Epidemics; Generative adversarial networks; Image processing; LSTM; Machine learning; Medical imaging; Monkeypox; Mpox; Neural networks; Skin; Skin lesions; Skin Lessions; Smallpox; Sytemic Disease; Viral diseases; Viruses; Zooming
• ISSN: 0948-695X; 0948-6968
• DOI: 10.3897/jucs.119234

Mpox is a disease like smallpox caused by the Mpox virus (MPXV), which belongs to the Orthopoxvirus (OPXV) group in the Poxviridae family. The virus is transmitted through direct contact with infected individuals, animals, or contaminated materials. Transmission can occur through direct body contact, animal bites, respiratory droplets, or mucous membranes in the eyes, nose, or mouth. However, since the recent outbreak in May 2022, the disease has spread to various countries, posing a threat to develop into a global pandemic. Several image processing and deep learning models, including Convolutional Neural Network (CNN), have been employed for Mpox disease prediction. The default CNN algorithm performs poorly on image orientations such as tilting, rotation, zooming, or other abnormal images. Therefore, we propose a new framework adopted from deep learning by combining Generative Adversarial Network (GAN), PyramidalNet, and Long Short-Term Memory (LSTM). This new method is referred to as GPLNet. The research results indicate that the GPLNet algorithm model can surpass the accuracy achieved by CNN and CNN-LSTM, reaching 99%. The performance of the GPLNet algorithm model is also evaluated using various measurement metrics, yielding an accuracy of 98%, precision of 99%, recall of 98%, sensitivity of 98%, specificity of 98%, f1-score of 98%, and ROC of 99%.