MPox-DenseConvNet: A Transfer Learning Based Convolutional Neural Network for Monkeypox Detection and Assessment using Color Models
Date
2023
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
IEEE
Abstract
Monkeypox, a zoonotic orthopoxvirus, unintentionally
produces smallpox-like sickness in people, though with a
far lower death rate. Despite the fact that Deep Networks
have been extensively used for visual inspection of such
diesases, the majority of works have frequently relied their
analysis on the results produced by a particular network
without taking the response of the colour channels to
classification findings into account. Deep learning has
recently shown to have enormous potential for image based diagnosis, including the detection of skin cancer, the
identification of tumour cells, and the COVID-19 patient
diagnosis through chest radiography. As a result, a similar
application may be used to identify the sickness associated
with monkeypox as it impacted human skin. This image
can then be obtained and employed to identify the illness.
This work focused on investing the prominent color
channel for Convolution Neural Network (ConvNet) based
monkeypox classification using skin images. For this
purpose, a transfer lerning based classification architecture
named MPox-DenseConvNet with fine tuning is designed.
Three colour channels namely RGB, HSV and YCbCr are
analyzed using proposed MPox-DenseConvNet. The
outcomes demonstrated that the colour channel employed
had an impact on the performance of the classification. The
results also confirmed that the HSV color channel has
outperformed of all the colour channels taken into
consideration.
Description
Keywords
Monkeypox, Convolutional Neural Network,
DenseNet, RGB, HSV, YCbCr
Citation
Tiwari, S., Maheshwari, P. and 2023 International Conference on Computational Intelligence and Knowledge Economy (ICCIKE) Dubai, United Arab Emirates 2023 March 9 - 2023 March 10 (2023) “MPox-DenseConvNet: A Transfer Learning Based Convolutional Neural Network for Monkeypox Detection and Assessment using Color Models,” in 2023 International Conference on Computational Intelligence and Knowledge Economy (ICCIKE), pp. 175–180.