ReviewInternational journal of environmental research and public health2023
Automated Monkeypox Skin Lesion Detection Using Deep Learning and Transfer Learning Techniques.
Review in International journal of environmental research and public health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 1 of them a synthesis that pooled it.
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Who cites it
28 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
- Federated learning with swarm intelligence for efficient and secure medical image analysis.Scientific reports · 2026Article
- Adaptive enhancement of chest X-ray images using tissue attenuation and local and global fusion.BMC bioinformatics · 2026Article
- A customized MobileNetV2-based lightweight CNN for monkeypox detection and classification.Scientific reports · 2026Article
- Advancing skin cancer diagnosis with deep learning and attention mechanisms.Scientific reports · 2025Article
- Deep Learning-Based Mpox Skin Lesion Detection and Real-Time Monitoring in a Smart Healthcare System.Diagnostics (Basel, Switzerland) · 2025Article
- Enhanced detection of Mpox using federated learning with hybrid ResNet-ViT and adaptive attention mechanisms.Scientific reports · 2025Article
- Breaking Diagnostic Barriers: Vision Transformers Redefine Monkeypox Detection.Diagnostics (Basel, Switzerland) · 2025Article
- Deep learning model for hair artifact removal and Mpox skin lesion analysis and detection.Scientific reports · 2025Article
- Mpox-XDE: an ensemble model utilizing deep CNN and explainable AI for monkeypox detection and classification.BMC infectious diseases · 2025Article
- Multi-Classification of Skin Lesion Images Including Mpox Disease Using Transformer-Based Deep Learning Architectures.Diagnostics (Basel, Switzerland) · 2025Article
- An optimized ensemble grey wolf-based pipeline for monkeypox diagnosis.Scientific reports · 2025Article
- Article
- DeepGenMon: A Novel Framework for Monkeypox Classification Integrating Lightweight Attention-Based Deep Learning and a Genetic Algorithm.Diagnostics (Basel, Switzerland) · 2025Article
- Perspective: advancing public health education by embedding AI literacy.Frontiers in digital health · 2025Article
- Can deep learning technology really recognize Mpox? A positive response from the EfficientNet model.Frontiers in microbiology · 2025Article
- MpoxNet: dual-branch deep residual squeeze and excitation monkeypox classification network with attention mechanism.Frontiers in cellular and infection microbiology · 2024Article
- Using novel deep learning models for rapid and efficient assistance in monkeypox screening from skin images.Frontiers in medicine · 2024Article
- Artificial Intelligence on Diagnostic Aid of Leprosy: A Systematic Literature Review.Journal of clinical medicine · 2023Review
- Recognition of Arabic Air-Written Letters: Machine Learning, Convolutional Neural Networks, and Optical Character Recognition (OCR) Techniques.Sensors (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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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.