Image Data collection and implementation of deep learning-based model in detecting Monkeypox disease using modified VGG16

Abstract

While the world is still attempting to recover from the damage caused by the broad spread of COVID-19, the monkeypox virus poses a new threat of becoming a global pandemic. Although the Monkeypox virus itself is not deadly and contagious as COVID-19, still every day, new patients case has been reported from many nations. Therefore, it will be no surprise if the world ever faces another global pandemic due to the lack of proper precautious steps. Recently, Machine learning (ML) has demonstrated huge potential in image-based diagnoses such as cancer detection, tumor cell identification, and COVID-19 patient detection. Therefore, a similar application can be adopted to diagnose the Monkeypox related disease as it infected the human skin, which image can be acquired and further used in diagnosing the disease. However, there is no publicly available Monkeypox dataset that can be used for training and experimenting the ML model development. Consequently, there is an immediate need to develop a dataset containing Monkeypox infected patients' images. Considering this opportunity, in this work, we introduce a newly developed "Monkeypox2022" dataset that is publicly available to use and can be obtained from our shared GitHub repository. The dataset is created by collecting images from multiple open-source and online portals that do not impose any restrictions on use, even for commercial purposes, hence giving a safer path to use and disseminate such data when constructing and deploying any type of ML models. Further, we propose and evaluate a modified VGG16 model, which includes two distinct studies: Study One and Two. Our exploratory computational results indicate that our suggested model can identify Monkeypox patients with an accuracy of 97 ± 1.8% (AUC = 97.2) and 88 ± 0.8% ( AUC = 0.867) for Study One and Two, respectively. Additionally, we explain our model's prediction and feature extraction utilizing Local

Deep Learning Models for the Detection of Monkeypox Skin Lesion on Digital Skin Images

“…The confusion matrices developed in the study affirm the reliability of these tests to produce accurate results. Another study on this topic is by [15], which evaluates a modified VGG-16 model. The researchers also sourced digital images from online sources and were keen only to select those with licenses.…”

Current and Perspective Sensing Methods for Monkeypox Virus

“…A monkeypox image dataset was constructed comprising 43 original images and 587 images obtained after data augmentation [ 34 ] ( Figure 3 ). Using the newly developed “Monkeypox 2022” dataset, an image classification model was proposed [ 35 ]. The study paves the way towards the development of image-analysis-based tools for monkeypox virus detection.…”

“…Redrawn from Ref. [ 35 ] with permission from the author. The source content is licensed under a Creative Commons Attributions 4.0 international license.…”

Deep learning Model for Recognizing Monkey Pox based on Dense net-121 Algorithm

“…AI-based methods provide a means of classifying features of a pox disease, then recognizing major features, which can be used to diagnose pox disease types such as Measles, Syphilis, chickenpox, or monkeypox (MPX). A proposed machine learning-based model is developed to recognize MPX using a modified Dense net-121 model and skin images dataset including MPX and Measles images [9]. Table 1 describes the used dataset, and Figure 1 depicts samples of this dataset.…”

“…In response to this shortcoming, this study tries to find an alternative solution to recognizing the MPX problem with better accuracy. In this paper, two algorithms, Dense net-121, and Convolution Neural Network (CNN) have been applied and compared on a developed dataset including MPX and Measles images [9]. The implementation results proved the superiority of the Dens net 121 algorithm compared to the CNN model in detecting MPX images.…”