The current outbreak of monkeypox (mpox) has become a major public health concern because of the quick spread of this disease across multiple countries. Early detection and diagnosis of mpox is crucial for effective treatment and management. Considering this, the purpose of this research was to detect and validate the best performing model for detecting mpox using deep learning approaches and classification models. To achieve this goal, we evaluated the performance of five common pretrained deep learning models (VGG19, VGG16, ResNet50, MobileNetV2, and EfficientNetB3) and compared their accuracy levels when detecting mpox. The performance of the models was assessed with metrics (i.e., the accuracy, recall, precision, and F1-score). Our experimental results demonstrate that the MobileNetV2 model had the best classification performance with an accuracy level of 98.16%, a recall of 0.96, a precision of 0.99, and an F1-score of 0.98. Additionally, validation of the model with different datasets showed that the highest accuracy of 0.94% was achieved using the MobileNetV2 model. Our findings indicate that the MobileNetV2 method outperforms previous models described in the literature in mpox image classification. These results are promising, as they show that machine learning techniques could be used for the early detection of mpox. Our algorithm was able to achieve a high level of accuracy in classifying mpox in both the training and test sets, making it a potentially valuable tool for quick and accurate diagnosis in clinical settings.
An outbreak of the 2019 novel Coronavirus epidemic (COVID-19) has rapidly spread worldwide. The coronavirus (COVID-19) has also spread among children, but it has been less severe than in adults. The characteristics of COVID-19 laboratory findings play a significant role in clinical manifestations, diagnosis, and treatment. Since the numbers of COVID-19 cases increased, it takes more time to interpret the lab outcomes and provide an accurate diagnosis. Little information about the clinical symptoms and epidemiological of COVID-19 is known. There is a need to investigate the characteristics of laboratory findings for the clinical decision-making system using predictive algorithms. This study aims to classify and validate machine learning approaches for detecting COVID-19 in children. The five well-known machine learning approaches: the artificial neural network (ANN); random forest (RF); support vector machines (SVM); decision trees (DT) which include classification and regression trees (CART); and gradient boosted trees (GBM) were used. All these approaches have been considered in the classification, and to determine the most suitable model. The performance of each model test was by conducted using a standard 10-fold cross-validation procedure. Given these results for classification performance and prediction of accuracy, CART is the best predictive model for classifications for children with COVID-19. The results of the study illustrate that the best classification performance was achieved with CART model to provide 92.5% accuracy for binary classes (positive vs. negative) based on laboratory findings. Leukocytes, Monocytes, Potassium, and Eosinophils, were among the most important predictors which indicate that those features may play a crucial role in COVID-19. Ultimately, our model may be helpful for medical experts to predict COVID-19 and can help invalidate their primary laboratory findings of children. ML methods can be a convenient tool for providing predictions for COVID-19 laboratory findings among Children.
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