ArticleJournal of medical systems2022
Monkeypox Virus Detection Using Pre-trained Deep Learning-based Approaches.
Article in Journal of medical systems, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
47 citing papers in PubMed.
- A customized MobileNetV2-based lightweight CNN for monkeypox detection and classification.Scientific reports · 2026Article
- AI-driven diagnosis of mpox using deep learning models.PloS one · 2026Article
- XMP-Net: An XAI-Based Modified Xception Model for Recognizing Monkeypox and Other Skin Diseases.BioMed research international · 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
- Poisson random measure noise-induced coherence in epidemiological priors informed deep neural networks to identify the intensity of virus dynamics.Scientific reports · 2025Article
- Mpox-XDE: an ensemble model utilizing deep CNN and explainable AI for monkeypox detection and classification.BMC infectious diseases · 2025Article
- Behavior changes influence mpox transmission in the United States, 2022-2023: Insights from homogeneous and heterogeneous models.PNAS nexus · 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
- Can deep learning technology really recognize Mpox? A positive response from the EfficientNet model.Frontiers in microbiology · 2025Article
- TMS: Ensemble Deep Learning Model for Accurate Classification of Monkeypox Lesions Based on Transformer Models with SVM.Diagnostics (Basel, Switzerland) · 2024Article
- Article
- Enhancing Disease Classification with Deep Learning: a Two-Stage Optimization Approach for Monkeypox and Similar Skin Lesion Diseases.Journal of imaging informatics in medicine · 2024Article
- Modelling the impact of human behavior using a two-layer Watts-Strogatz network for transmission and control of Mpox.BMC infectious diseases · 2024Article
- Using novel deep learning models for rapid and efficient assistance in monkeypox screening from skin images.Frontiers in medicine · 2024Article
- Exploring the dynamics of monkeypox transmission with data-driven methods and a deterministic model.Frontiers in epidemiology · 2024Article
- MpoxNet: dual-branch deep residual squeeze and excitation monkeypox classification network with attention mechanism.Frontiers in cellular and infection microbiology · 2024Article
- Evaluating Retinal Disease Diagnosis with an Interpretable Lightweight CNN Model Resistant to Adversarial Attacks.Journal of imaging · 2023Article
- Hyper-parameter tuned deep learning approach for effective human monkeypox disease detection.Scientific reports · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Monkeypox virus is emerging slowly with the decline of COVID-19 virus infections around the world. People are afraid of it, thinking that it would appear as a pandemic like COVID-19. As such, it is crucial to detect them earlier before widespread community transmission. AI-based detection could help identify them at the early stage. In this paper, we aim to compare 13 different pre-trained deep learning (DL) models for the Monkeypox virus detection. For this, we initially fine-tune them with the addition of universal custom layers for all of them and analyse the results using four well-established measures: Precision, Recall, F1-score, and Accuracy. After the identification of the best-performing DL models, we ensemble them to improve the overall performance using a majority voting over the probabilistic outputs obtained from them. We perform our experiments on a publicly available dataset, which results in average Precision, Recall, F1-score, and Accuracy of 85.44%, 85.47%, 85.40%, and 87.13%, respectively with the help of our proposed ensemble approach. These encouraging results, which outperform the state-of-the-art methods, suggest that the proposed approach is applicable to health practitioners for mass screening.
Indexed as
Identifiers
What OpenQuestion holds
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.