ArticleDiagnostics (Basel, Switzerland)2023
Detection of Monkeypox Disease from Human Skin Images with a Hybrid Deep Learning Model.
Article in Diagnostics (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
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Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it, 72 citations in OpenAlex.
- Use of AI in Identification of Sexually Transmitted Infections and Anogenital Dermatoses: A Systematic Review and Meta-Analysis.JAMA network open · 2025Pooled it
- Test-fairness deep learning with influence score.PLOS digital health · 2026Article
- XMP-Net: An XAI-Based Modified Xception Model for Recognizing Monkeypox and Other Skin Diseases.BioMed research international · 2026Article
- AI-driven diagnosis of mpox using deep learning models.PloS one · 2026Article
- Enhancing Monkeypox Diagnosis with Transformers: Bridging Explainability and Performance with Quantitative Validation.Diagnostics (Basel, Switzerland) · 2025Article
- Deep learning model for hair artifact removal and Mpox skin lesion analysis and detection.Scientific reports · 2025Article
- Explainable AI for Symptom-Based Detection of Monkeypox: a machine learning approach.BMC infectious diseases · 2025Article
- Mpox-XDE: an ensemble model utilizing deep CNN and explainable AI for monkeypox detection and classification.BMC infectious diseases · 2025Article
- Capsule network approach for monkeypox (CAPSMON) detection and subclassification in medical imaging system.Scientific reports · 2025Article
- Intelligent skin disease prediction system using transfer learning and explainable artificial intelligence.Scientific reports · 2025Article
- Robustly detecting mpox and non-mpox using a deep learning framework based on image inpainting.Scientific reports · 2025Article
- Advancements in Artificial Intelligence for Medical Computer-Aided Diagnosis.Diagnostics (Basel, Switzerland) · 2024Article
- Leveraging deep neural networks to uncover unprecedented levels of precision in the diagnosis of hair and scalp disorders.Skin research and technology : official journal of International Society for Bioengineering and the Skin (ISBS) [and] International Society for Digital Imaging of Skin (ISDIS) [and] International Society for Skin Imaging (ISSI) · 2024Article
- Article
- MRpoxNet: An enhanced deep learning approach for early detection of monkeypox using modified ResNet50.Digital healthArticle
Corrections and comments
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Authors and funding
1 author at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Monkeypox, a virus transmitted from animals to humans, is a DNA virus with two distinct genetic lineages in central and eastern Africa. In addition to zootonic transmission through direct contact with the body fluids and blood of infected animals, monkeypox can also be transmitted from person to person through skin lesions and respiratory secretions of an infected person. Various lesions occur on the skin of infected individuals. This study has developed a hybrid artificial intelligence system to detect monkeypox in skin images. An open source image dataset was used for skin images. This dataset has a multi-class structure consisting of chickenpox, measles, monkeypox and normal classes. The data distribution of the classes in the original dataset is unbalanced. Various data augmentation and data preprocessing operations were applied to overcome this imbalance. After these operations, CSPDarkNet, InceptionV4, MnasNet, MobileNetV3, RepVGG, SE-ResNet and Xception, which are state-of-the-art deep learning models, were used for monkeypox detection. In order to improve the classification results obtained in these models, a unique hybrid deep learning model specific to this study was created by using the two highest-performing deep learning models and the long short-term memory (LSTM) model together. In this hybrid artificial intelligence system developed and proposed for monkeypox detection, test accuracy was 87% and Cohen's kappa score was 0.8222.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.