A Robust Ensemble of Convolutional Neural Networks for the Detection of Monkeypox Disease from Skin Images.

Monkeypox is a smallpox-like disease that was declared a global health emergency in July 2022. Because of this resemblance, it is not easy to distinguish a monkeypox rash from other similar diseases; however, due to the novelty of this disease, there are no widely used databases for this purpose with which to develop image-based classification algorithms. Therefore, three significant contributions are proposed in this work: first, the development of a publicly available dataset of monkeypox images; second, the development of a classification system based on convolutional neural networks in order to automatically distinguish monkeypox marks from those produced by other diseases; and, finally, the use of explainable AI tools for ensemble networks. For point 1, free images of monkeypox cases and other diseases have been searched in government databases and processed until we are left with only a section of the skin of the patients in each case. For point 2, various pre-trained models were used as classifiers and, in the second instance, combinations of these were used to form ensembles. And, for point 3, this is the first documented time that an explainable AI technique (like GradCAM) is applied to the results of ensemble networks. Among all the tests, the accuracy reaches 93% in the case of single pre-trained networks, and up to 98% using an ensemble of three networks (ResNet50, EfficientNetB0, and MobileNetV2). Comparing these results with previous work, a substantial improvement in classification accuracy is observed.

Monkeypox detection from skin lesion images using an amalgamation of CNN models aided with Beta function-based normalization scheme.

We have recently been witnessing that our society is starting to heal from the impacts of COVID-19. The economic, social and cultural impacts of a pandemic cannot be ignored and we should be properly equipped to deal with similar situations in future. Recently, Monkeypox has been concerning the international health community with its lethal impacts for a probable pandemic. In such situations, having appropriate protocols and methodologies to deal with the outbreak efficiently is of paramount interest to the world. Early diagnosis and treatment stand as the only viable option to tackle such problems. To this end, in this paper, we propose an ensemble learning-based framework to detect the presence of the Monkeypox virus from skin lesion images. We first consider three pre-trained base learners, namely Inception V3, Xception and DenseNet169 to fine-tune on a target Monkeypox dataset. Further, we extract probabilities from these deep models to feed into the ensemble framework. To combine the outcomes, we propose a Beta function-based normalization scheme of probabilities to learn an efficient aggregation of complementary information obtained from the base learners followed by the sum rule-based ensemble. The framework is extensively evaluated on a publicly available Monkeypox skin lesion dataset using a five-fold cross-validation setup to evaluate its effectiveness. The model achieves an average of 93.39%, 88.91%, 96.78% and 92.35% accuracy, precision, recall and F1 scores, respectively. The supporting source codes are presented in https://github.com/BihanBanerjee/MonkeyPox.

MonkeyNet: A robust deep convolutional neural network for monkeypox disease detection and classification.

The monkeypox virus poses a new pandemic threat while we are still recovering from COVID-19. Despite the fact that monkeypox is not as lethal and contagious as COVID-19, new patient cases are recorded every day. If preparations are not made, a global pandemic is likely. Deep learning (DL) techniques are now showing promise in medical imaging for figuring out what diseases a person has. The monkeypox virus-infected human skin and the region of the skin can be used to diagnose the monkeypox early because an image has been used to learn more about the disease. But there is still no reliable Monkeypox database that is available to the public that can be used to train and test DL models. As a result, it is essential to collect images of monkeypox patients. The "MSID" dataset, short form of "Monkeypox Skin Images Dataset", which was developed for this research, is free to use and can be downloaded from the Mendeley Data database by anyone who wants to use it. DL models can be built and used with more confidence using the images in this dataset. These images come from a variety of open-source and online sources and can be used for research purposes without any restrictions. Furthermore, we proposed and evaluated a modified DenseNet-201 deep learning-based CNN model named MonkeyNet. Using the original and augmented datasets, this study suggested a deep convolutional neural network that was able to correctly identify monkeypox disease with an accuracy of 93.19% and 98.91% respectively. This implementation also shows the Grad-CAM which indicates the level of the model's effectiveness and identifies the infected regions in each class image, which will help the clinicians. The proposed model will also help doctors make accurate early diagnoses of monkeypox disease and protect against the spread of the disease.

[18F]-Fluorodeoxyglucose Uptake in Lymphoid Tissue Serves as a Predictor of Disease Outcome in the Nonhuman Primate Model of Monkeypox Virus Infection.

Real-time bioimaging of infectious disease processes may aid countermeasure development and lead to an improved understanding of pathogenesis. However, few studies have identified biomarkers for monitoring infections using in vivo imaging. Previously, we demonstrated that positron emission tomography/computed tomography (PET/CT) imaging with [18F]-fluorodeoxyglucose (FDG) can monitor monkeypox disease progression in vivo in nonhuman primates (NHPs). In this study, we investigated [18F]-FDG-PET/CT imaging of immune processes in lymphoid tissues to identify patterns of inflammation in the monkepox NHP model and to determine the value of [18F]-FDG-PET/CT as a biomarker for disease and treatment outcomes. Quantitative analysis of [18F]-FDG-PET/CT images revealed differences between moribund and surviving animals at two sites vital to the immune response to viral infections, bone marrow and lymph nodes (LNs). Moribund NHPs demonstrated increased [18F]-FDG uptake in bone marrow 4 days postinfection compared to surviving NHPs. In surviving, treated NHPs, increase in LN volume correlated with [18F]-FDG uptake and peaked 10 days postinfection, while minimal lymphadenopathy and higher glycolytic activity were observed in moribund NHPs early in infection. Imaging data were supported by standard virology, pathology, and immunology findings. Even with the limited number of subjects, imaging was able to differentiate the difference between disease outcomes, warranting additional studies to demonstrate whether [18F]-FDG-PET/CT can identify other, subtler effects. Visualizing altered metabolic activity at sites involved in the immune response by [18F]-FDG-PET/CT imaging is a powerful tool for identifying key disease-specific time points and locations that are most relevant for pathogenesis and treatment.IMPORTANCE Positron emission tomography and computed tomography (PET/CT) imaging is a universal tool in oncology and neuroscience. The application of this technology to infectious diseases is far less developed. We used PET/CT imaging with [18F]-labeled fluorodeoxyglucose ([18F]-FDG) in monkeys after monkeypox virus exposure to monitor the immune response in lymphoid tissues. In lymph nodes of surviving monkeys, changes in [18F]-FDG uptake positively correlated with enlargement of the lymph nodes and peaked on day 10 postinfection. In contrast, the bone marrow and lymph nodes of nonsurvivors showed increased [18F]-FDG uptake by day 4 postinfection with minimal lymph node enlargement, indicating that elevated cell metabolic activity early after infection is predictive of disease outcome. [18F]-FDG-PET/CT imaging can provide real-time snapshots of metabolic activity changes in response to viral infections and identify key time points and locations most relevant for monitoring the development of pathogenesis and for potential treatment to be effective.

Evaluation of monkeypox disease progression by molecular imaging.

Infection of nonhuman primates (NHPs) with monkeypox virus (MPXV) is currently being developed as an animal model of variola infection in humans. We used positron emission tomography and computed tomography (PET/CT) to identify inflammatory patterns as predictors for the outcome of MPXV disease in NHPs. Two NHPs were sublethally inoculated by the intravenous (IV) or intrabronchial (IB) routes and imaged sequentially using fluorine-18 fluorodeoxyglucose ((18)FDG) uptake as a nonspecific marker of inflammation/immune activation. Inflammation was observed in the lungs of IB-infected NHPs, and bilobular involvement was associated with morbidity. Lymphadenopathy and immune activation in the axillary lymph nodes were evident in IV- and IB-infected NHPs. Interestingly, the surviving NHPs had significant (18)FDG uptake in the axillary lymph nodes at the time of MPXV challenge with no clinical signs of illness, suggesting an association between preexisting immune activation and survival. Molecular imaging identified patterns of inflammation/immune activation that may allow risk assessment of monkeypox disease.