Deep ensemble learning for chickenpox detection from clinical images

Seyyed Kamran Hosseini 1, *, Faraidoon Habibi 2 and Naqibullah Vakil I 2

1 Department of Software Engineering, Computer Science Faculty, Herat university, Herat, Afghanistan.
2 Department of Network Engineering, Computer science Faculty, Herat University, Herat, Afghanistan.
 
Review
International Journal of Science and Research Archive, 2024, 12(01), 344–352.
Article DOI: 10.30574/ijsra.2024.12.1.0771
Publication history: 
Received on 23 March 2024; revised on 03 May 2024; accepted on 06 May 2024
 
Abstract: 
Chickenpox caused by the varicella-zoster virus is an extremely contagious viral infection common in children and quickly develops into a severe problem. Over 90% of unvaccinated people have been infected. Still, infection occurs at different ages in different parts of the world- Over 70 % of people become infected by the age of 10 years in the United States, the United Kingdom, and Japan, and by the age of 20 in India, West Indies, and South East Asia. Automatic classification of the specific disease is a challenging task to present clinicians to distinguish between different kinds of skin conditions and recommend suitable treatment. Convolutional Neural Networks have recently achieved great success in many machines learning purposes and have presented a state-of-the-art performance in various computer-assisted diagnosis applications. This study proposes a deep neural network-based method that follows an ensemble approach by combining VGG-16, VGG-19, and ResNet-50 architectures to distinguish chickenpox from other skin conditions. Experimental test results have achieved accurate classification with an assuring test accuracy up to 93%.
 
Keywords: 
Chickenpox; Ensemble; Classification; Deep Learning; Convolution Neural Network
 
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