Abstract:
As the world continues to recover from COVID-19, the virus monkeypox provides a new
pandemic intimidation. Monkeypox isn't as deadly or as widespread as the virus of
COVID-19, but some new cases are reported regularly from the several nations. Without
sufficient precautions, it shouldn't come as a fact if another worldwide pandemic occurs.
The efficiency of an automated deep-learning algorithm for categorization was evaluated
using the monkeypox dataset. The main objective is to create a deep learning model that
will understand the different datasets as accurately as appropriate. First, similar image
preprocessing techniques are applied to enhance brightness and contrast for this dataset.
As the datasets utilized in this experiment contain few images to effectively train a deep
learning model, data augmentation is applied. Then, a VGG-16 model with randomly
selected hyperparameters and layer numbers is generated. VGG16, VGG19, MobileNetV2,
and ResNet50 are evaluated to determine whether the model yielded the highest
performance. VGG16 is utilized as the foundational model because it has the best accuracy.
In this work, VGG-16 was utilized to assess its robustness and get the highest accuracy
level obtainable. Using image preprocessing algorithms with appropriate parameter values,
image quality is enhanced. A total of 770 preprocessed images of monkeypox were
augmented using seven approaches, yielding a collection of 1991 images. The model was
then evaluated for its robustness. Results were compared to those obtained from previous
research. Within a training accuracy of 97.29%, a validation accuracy of 95.26%, and a test
accuracy of 96.29%, the VGG-16 model performed the best. VGG19 achieved test
accuracy of 95.06%, MobileNetV2 of 94.59%, and ResNet50 of 54.32%. Our proposed
algorithm, which relies on preprocessing of images, transfer learning, and adjustment,
which shown a high level of accuracy in identifying monkeypox based on a small set of
intricate photos. After determining appropriate configuration, this model is trained by using
remaining of the data set to evaluate overall performance. We generate and evaluate
different performance measures for the monkeypox dataset, including accuracy, precision,
recall, specificity, and F1-score.
