MonkeypoxHybridNet: A Hybrid Deep Convolutional Neural Network Model for Monkeypox Disease Detection

Abstract: Following the COVID-19 pandemic, the recent spread of the monkeypox virus beyond West and Central Africa has become a growing public health concern, and rapid, reliable detection of the disease from skin lesion images is therefore important. This chapter proposes MonkeypoxHybridNet, a new hybrid deep convolutional neural network (CNN) model that combines the ResNet50, VGG19 and InceptionV3 architectures for monkeypox disease detection. The model is trained and tested on a publicly available monkeypox skin lesion dataset with four classes (chickenpox, measles, monkeypox and normal), comprising 580 training and 190 test images, and is evaluated with accuracy, precision and F1-score. ResNet50, VGG19 and InceptionV3 achieve accuracies of 0.595, 0.705 and 0.805, respectively, whereas the proposed MonkeypoxHybridNet achieves the highest accuracy of 0.842, showing that it is successful in detecting monkeypox from skin images. New models and hybrid architectures for monkeypox detection are suggested for future research.

Keywords: Monkeypox, Deep learning, Convolutional neural networks, Hybrid CNN, Disease detection, Medical image classification

Publication details: Book chapter in: International Research in Engineering Sciences III, edited by Mehmet Kamanlı, pp. 49-64. Eğitim Publishing (Eğitim Yayınevi), Konya, Türkiye, November-December 2022. ISBN: 978-625-6382-25-1.

How to cite / Kaynak gösterimi

APA 7: Gülmez, B. (2022). MonkeypoxHybridNet: A Hybrid Deep Convolutional Neural Network Model for Monkeypox Disease Detection. In M. Kamanlı (Ed.), International Research in Engineering Sciences III (pp. 49–64). Eğitim Publishing (Eğitim Yayınevi). https://doi.org/10.5281/zenodo.23037721

IEEE: B. Gülmez, "MonkeypoxHybridNet: A Hybrid Deep Convolutional Neural Network Model for Monkeypox Disease Detection," in International Research in Engineering Sciences III, M. Kamanlı, Ed. Konya, Türkiye: Eğitim Publishing (Eğitim Yayınevi), 2022, pp. 49–64, doi: 10.5281/zenodo.23037721.

Chicago: Gülmez, Burak. 2022. "MonkeypoxHybridNet: A Hybrid Deep Convolutional Neural Network Model for Monkeypox Disease Detection." In International Research in Engineering Sciences III, edited by Mehmet Kamanlı, 49–64. Konya, Türkiye: Eğitim Publishing (Eğitim Yayınevi). https://doi.org/10.5281/zenodo.23037721.

MLA 9: Gülmez, Burak. "MonkeypoxHybridNet: A Hybrid Deep Convolutional Neural Network Model for Monkeypox Disease Detection." International Research in Engineering Sciences III, edited by Mehmet Kamanlı, Eğitim Publishing (Eğitim Yayınevi), 2022, pp. 49–64. https://doi.org/10.5281/zenodo.23037721.

Harvard: Gülmez, B. (2022) 'MonkeypoxHybridNet: A Hybrid Deep Convolutional Neural Network Model for Monkeypox Disease Detection', in M. Kamanlı (Ed.), International Research in Engineering Sciences III. Konya, Türkiye: Eğitim Publishing (Eğitim Yayınevi), pp. 49–64. doi: 10.5281/zenodo.23037721.

BibTeX:

@incollection{Gulmez2022monkeypoxhybridnet:,
  author    = {Gülmez, Burak},
  title     = {MonkeypoxHybridNet: A Hybrid Deep Convolutional Neural Network Model for Monkeypox Disease Detection},
  booktitle = {International Research in Engineering Sciences III},
  editor    = {Kamanlı, Mehmet},
  pages     = {49--64},
  year      = {2022},
  publisher = {Eğitim Publishing (Eğitim Yayınevi)},
  address   = {Konya, Türkiye},
  isbn      = {978-625-6382-25-1},
  doi       = {10.5281/zenodo.23037721}
}