ArticleExpert systems with applications2023
Deep transfer learning approaches for Monkeypox disease diagnosis.
Article in Expert systems with applications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Use of AI in Identification of Sexually Transmitted Infections and Anogenital Dermatoses: A Systematic Review and Meta-Analysis.JAMA network open · 2025Pooled it
- NextGen lung disease diagnosis with explainable artificial intelligence.Scientific reports · 2025Article
- Breaking Diagnostic Barriers: Vision Transformers Redefine Monkeypox Detection.Diagnostics (Basel, Switzerland) · 2025Article
- Comprehensive Genomic, Mutation, Phylogenetic, and Statistical Analysis of the Monkeypox Virus Across Multiple Countries.Indian journal of microbiology · 2025Article
- Robustly detecting mpox and non-mpox using a deep learning framework based on image inpainting.Scientific reports · 2025Article
- DeepGenMon: A Novel Framework for Monkeypox Classification Integrating Lightweight Attention-Based Deep Learning and a Genetic Algorithm.Diagnostics (Basel, Switzerland) · 2025Article
- Comparative analysis of deep learning and radiomics models in predicting hepatocellular carcinoma differentiation via ultrasound.Frontiers in medicine · 2025Article
- BrainNet: a fusion assisted novel optimal framework of residual blocks and stacked autoencoders for multimodal brain tumor classification.Scientific reports · 2024Article
- MpoxNet: dual-branch deep residual squeeze and excitation monkeypox classification network with attention mechanism.Frontiers in cellular and infection microbiology · 2024Article
- SkinNet-INIO: Multiclass Skin Lesion Localization and Classification Using Fusion-Assisted Deep Neural Networks and Improved Nature-Inspired Optimization Algorithm.Diagnostics (Basel, Switzerland) · 2023Article
- Article
- Detection of Monkeypox Cases Based on Symptoms Using XGBoost and Shapley Additive Explanations Methods.Diagnostics (Basel, Switzerland) · 2023Article
- Diagnosis of Monkeypox Disease Using Transfer Learning and Binary Advanced Dipper Throated Optimization Algorithm.Biomimetics (Basel, Switzerland) · 2023Article
- Detection of Monkeypox Disease from Human Skin Images with a Hybrid Deep Learning Model.Diagnostics (Basel, Switzerland) · 2023Article
- Nomograms for Predicting the Risk and Prognosis of Liver Metastases in Pancreatic Cancer: A Population-Based Analysis.Journal of personalized medicine · 2023Article
- A review on the use of machine learning techniques in monkeypox disease prediction.Science in One Health · 2023Review
- Early detection of human Mpox: A comparative study by using machine learning and deep learning models with ensemble approach.Digital healthArticle
- Harnessing Artificial Intelligence and Innovative Vaccines for Mpox Diagnosis and Control: A Comprehensive Narrative Review.Journal of primary care & community healthReview
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Monkeypox has become a significant global challenge as the number of cases increases daily. Those infected with the disease often display various skin symptoms and can spread the infection through contamination. Recently, Machine Learning (ML) has shown potential in image-based diagnoses, such as detecting cancer, identifying tumor cells, and identifying coronavirus disease (COVID)-19 patients. Thus, ML could potentially be used to diagnose Monkeypox as well. In this study, we developed a Monkeypox diagnosis model using Generalization and Regularization-based Transfer Learning approaches (GRA-TLA) for binary and multiclass classification. We tested our proposed approach on ten different convolutional Neural Network (CNN) models in three separate studies. The preliminary computational results showed that our proposed approach, combined with Extreme Inception (Xception), was able to distinguish between individuals with and without Monkeypox with an accuracy ranging from 77% to 88% in Studies One and Two, while Residual Network (ResNet)-101 had the best performance for multiclass classification in Study Three, with an accuracy ranging from 84% to 99%. In addition, we found that our proposed approach was computationally efficient compared to existing TL approaches in terms of the number of parameters (NP) and Floating-Point Operations per Second (FLOPs) required. We also used Local Interpretable Model-Agnostic Explanations (LIME) to explain our model's predictions and feature extractions, providing a deeper understanding of the specific features that may indicate the onset of Monkeypox.
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