ArticleJournal of medical systems2022
Human Monkeypox Classification from Skin Lesion Images with Deep Pre-trained Network using Mobile Application.
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 37 papers, 1 of them a synthesis that pooled it.
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Who cites it
37 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
- 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
- Emerging threats of monkeypox virus in 2024: current insights and future directions.Future science OA · 2025Review
- Development of machine learning-based mpox surveillance models in a learning health system.Sexually transmitted infections · 2025Article
- Deep Learning-Based Mpox Skin Lesion Detection and Real-Time Monitoring in a Smart Healthcare System.Diagnostics (Basel, Switzerland) · 2025Article
- Poisson random measure noise-induced coherence in epidemiological priors informed deep neural networks to identify the intensity of virus dynamics.Scientific reports · 2025Article
- Capsule network approach for monkeypox (CAPSMON) detection and subclassification in medical imaging system.Scientific reports · 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
- Using AI to Differentiate Mpox From Common Skin Lesions in a Sexual Health Clinic: Algorithm Development and Validation Study.Journal of medical Internet research · 2024Article
- Human monkeypox disease prediction using novel modified restricted Boltzmann machine-based equilibrium optimizer.Scientific reports · 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
- MpoxNet: dual-branch deep residual squeeze and excitation monkeypox classification network with attention mechanism.Frontiers in cellular and infection microbiology · 2024Article
- Hyper-parameter tuned deep learning approach for effective human monkeypox disease detection.Scientific reports · 2023Article
- Article
- Utilizing convolutional neural networks to classify monkeypox skin lesions.Scientific reports · 2023Article
- Detection of Monkeypox Cases Based on Symptoms Using XGBoost and Shapley Additive Explanations Methods.Diagnostics (Basel, Switzerland) · 2023Article
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recently, human monkeypox outbreaks have been reported in many countries. According to the reports and studies, quick determination and isolation of infected people are essential to reduce the spread rate. This study presents an Android mobile application that uses deep learning to assist this situation. The application has been developed with Android Studio using Java programming language and Android SDK 12. Video images gathered through the mobile device's camera are dispatched to a deep convolutional neural network that runs on the same device. Camera2 API of the Android platform has been used for camera access and operations. The network then classifies images as positive or negative for monkeypox detection. The network's training has been carried out using skin lesion images of monkeypox-infected people and other skin lesion images. For this purpose, a publicly available dataset and a deep transfer learning approach have been used. All training and testing steps have been applied on Matlab using different pre-trained networks. Then, the network that has the best accuracy has been recreated and trained using TensorFlow. The TensorFlow model has been adapted to mobile devices by converting to the TensorFlow Lite model. The TensorFlow Lite model has been then embedded into the mobile application together with the TensorFlow Lite library for monkeypox detection. The application has been run on three devices successfully. During the run-time, the inference times have been gathered. 197 ms, 91 ms, and 138 ms average inference times have been observed. The presented system allows people with body lesions to quickly make a preliminary diagnosis. Thus, monkeypox-infected people can be encouraged to act rapidly to see an expert for a definitive diagnosis. According to the test results, the system can classify the images with 91.11% accuracy. In addition, the proposed mobile application can be trained for the preliminary diagnosis of other skin diseases.
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