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In some cases, a doctor may recommend other treatments to address chickenpox complications. For example, a baby with dehydration from a high fever and inadequate liquid intake might need to receive intravenous fluids in a hospital.

Chickenpox Images Download Free

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Very rarely, a person with a weakened immune system may get a second infection. It is also possible for the virus to reactivate, but in most cases, reactivation of the virus causes shingles rather than chickenpox.

Chickenpox lives in the respiratory tract and eyes. It is highly contagious in people who have active infections and in cases of recent exposure. A person who is not immune can get chickenpox if they come into contact with mucus, saliva, or other bodily fluids from a person with the infection.

People living in close quarters are particularly vulnerable, as are unvaccinated children in preschools and daycare centers. A pregnant woman with chickenpox can also transmit the virus to her newborn.

Vaccination remains the best strategy for reducing the risk of chickenpox in babies and young children. As babies under the age of 1 year do not receive a vaccine against chickenpox, the safest practice is for everyone else around them to get the vaccine. Parents and caregivers can discuss prevention and safety strategies with a healthcare professional.

Chickenpox, otherwise referred to as varicella, is a severe and highly transmissible disease caused by the herpes virus varicella-zoster virus (VZV). Research has identified only one serotype of VZV, and humans are its only known reservoir. Chickenpox occurs most frequently in people over 50 or those with impaired immune systems [14]. Hence, it is predominant in babies but can also be found in adolescents, adults, pregnant women, and immunocompromised people [15]. Chickenpox manifest as itchy rashes with fluid-like blisters similar to monkeypox, measles, and skin cancer [16]. Over the course of several days, the blisters may pop up and start to leak. Then, they crust and scab over before healing [16]. Chickenpox can be transmitted by breathing in particles from chickenpox blisters or by being in contact with someone who has it [14]. Children in temperate regions are more likely to contract chickenpox than adults, with those in elementary school and younger being most at risk. The normal seasons for the disease are the end of winter and the beginning of spring. Compared to temperate regions, where infections peak in early childhood, adults in tropical regions, notably less populated areas, are more susceptible to these diseases. In tropical settings, the wettest and coolest months are when the highest rates of infection occur [17].

Gambian pouched rats and dormice, introduced to the nation from Ghana, have been living alongside these pets. More than 70 people in the United States contracted monkeypox because of this epidemic. Nigerian tourists have been diagnosed with monkeypox in the following countries: Israel (September 2018), the United Kingdom (September 2018, December 2019, May 2021, and May 2022), Singapore (May 2019), and the United States of America (July 2021, and November 2022). Multiple cases of monkeypox were discovered in various non-endemic nations in May of 2022 [18]. Figure 1 shows the lessons associated with monkeypox and chickenpox.

Due to the similarity of the lesions displayed in the early stages of monkeypox and chickenpox, a clinical diagnosis of monkeypox is difficult. A single misdiagnosis could impact the larger communities as they are both contagious diseases and could spread faster through skin contact and several other routes. They could also result in wrong vaccination and treatment, which can be costly to the government, aid organizations, and infected patients. Even though the mortality rate of the two diseases is relatively low, the spread of the virus could get to immunocompromised patients, which can lead to their deaths. Besides immunocompromised patients, children and older people with low immunity could also get infected with the virus, resulting in death.

In a feasibility study by Nafisa Ali et al. [22], several pre-trained deep-learning models were used to detect monkeypox and other similar chickenpox and measles lesions. The dataset was obtained from publicly accessible websites and news portals, and a data augmentation technique was used to increase the total number of datasets. Three commonly used pre-trained DL models were used, including Inception V3, ResNet50, and VGG-16. The result was satisfactory in differentiating monkeypox lesions from other forms of lesions associated with measles and chickenpox. The ResNet50 model recorded the highest performance, with an accuracy of 82.96%. VGG16 and an ensemble of the three models produced an accuracy of 81.48 and 79.26, respectively. In another study by Islam et al. [23], the notion of whether AI can detect monkeypox lesions from digital skin images was investigated. The study introduced the monkeypox skin image dataset in 2022, which is the largest so far [23]. The study implements seven DL models, including ResNet50, DenseNet21, Inception-V3, Squeeze Net, MnasNet-AI, MobileNet-V2, and ShuffleNet-V2-1X. The study concludes that AI has great potential in detecting monkeypox from digital skin images, with a precision rate of 85%. Adler et al. [24] elaborate on the clinical features and management of human monkeypox in a retrospective observational study in the United Kingdom (UK). The study concludes that human monkeypox poses unique challenges in the UK, and even to well-resourced healthcare systems with high-consequence infectious diseases (HCID) networks.

Lee et al. [25] conducted a study on applying a deep-learning model to predict chickenpox. The data used were extracted through web scraping using keywords related to chickenpox. Linear regression and long short-term memory (LSTM) were used to predict chickenpox over time, and the model generated a satisfactory correlation coefficient of 0.97114. However, the linear regression model generates a higher root mean square error of 341.01547. In another study by Alakus & Baykara [26], a DL algorithm was implemented to classify human papilloma virus (HPV) causing monkeypox virus (MPV) and monkeypox DNA sequences. Several DNA mapping methods were evaluated using accuracy, precision, recall, and F1 scores. The result obtained indicates an average accuracy of 96.08% and an F1 score of 99.83%. This further shows that the application of DNA sequences for the classification of warts and monkeypox is feasible, practical, and accurate. Ramadhan and Baykara, [27] implemented an image cropping method and VGG16 model to conduct a binary and multiple classification of coronavirus (COVID-19), normal, and pneumonia. The model produced an accuracy of 97.5% and 99.76% for multiple and binary classifications.

Additionally, the original chickenpox and monkeypox dataset underwent an augmentation process utilizing the Python Imaging Library (PIL) version 9.2.0 and the Scikit-image library version 0.19.3. Most state-of-the-art models contain many parameters in the order of millions. To train a model for accurate results, more parameters are needed to learn almost all the features from the data. We need a good amount of data to accommodate all these parameters, and it is standard that DL models often require more data which is only sometimes available. Hence, increasing the number of images and adding some variability to the data is necessary. This technique increased the post-augmentation number of chickenpox and monkeypox images by approximately 44- and 42-fold, resulting in 10,000 images each, as shown in Table 1. However, 50 images each from the monkeypox and chickenpox classes were set aside without augmentation for test data. This will be appropriate for evaluating the performance of the model on unseen skin lesion images of monkeypox and chickenpox. Also, setting aside unseen and un-augmented images for model evaluation prevents knowledge leakage to the model, thereby preventing overfitting.

Image denoising is a process of removing the noise from an image [32]. If not properly addressed, noise from an image will cause a loss of information [32]. This noise often comes from pre-image processing procedures which may include images captured in a low-light situation, sensor illumination levels of a digital camera, faulty memory locations in hardware, and errors in the transmission of data over long distances, etc. Hence, image noise removal is vital and essential to recover the original image from degraded ones. We adopted a deep CNN autoencoder of Denise images present in the dataset, which is trained to reconstruct its input image by learning useful features and representation of the data through an unsupervised learning process. It consists of an encoder and a decoder, both of which are deep neural networks. The encoder networks take in an input image and convert it into a compact representation, known as the latent code, which is then passed to the decoder networks. The decoder network processes the latent code and produces the reconstruction of the input image.

Nonetheless, 1 image of chickenpox was incorrectly classified as monkeypox (False Positive (FP)), while no image of monkeypox was incorrectly classified as chickenpox (False Negative (FN)). False positive tells how many times a model incorrectly classifies chickenpox as monkeypox. In contrast, false negative tells how many times a model incorrectly classifies monkeypox and chickenpox. This further clarifies that the model performs incredibly well and may be combined with other known symptoms for the final diagnosis of the disease. 17dc91bb1f