Related Experiment Video
Updated: Aug 7, 2025
08:20
Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
Published on: October 27, 2023
1.5K
Automated Monkeypox Skin Lesion Detection Using Deep Learning and Transfer Learning Techniques
Ameera S Jaradat1, Rabia Emhamed Al Mamlook2,3, Naif Almakayeel4
1Department of Computer Science, Information Technology and Computer Science, Yarmouk University, Irbid 211633, Jordan.
Summary
This study identified MobileNetV2 as the top deep learning model for detecting monkeypox (mpox) from images, achieving 98.16% accuracy. This advancement aids in the early diagnosis and management of mpox outbreaks.
Area of Science:
- Medical Informatics
- Computer Science
- Virology
Background:
- The global spread of monkeypox (mpox) necessitates rapid and accurate diagnostic tools.
- Early detection is critical for effective mpox treatment and public health management.
Purpose of the Study:
- To evaluate and validate deep learning models for detecting mpox.
- To identify the best-performing model for mpox image classification.
Main Methods:
- Five pretrained deep learning models (VGG19, VGG16, ResNet50, MobileNetV2, EfficientNetB3) were assessed.
- Model performance was quantified using accuracy, recall, precision, and F1-score.
- The top model was validated on diverse datasets.
Main Results:
- MobileNetV2 demonstrated superior performance with 98.16% accuracy, 0.96 recall, 0.99 precision, and 0.98 F1-score.
- External validation confirmed MobileNetV2's efficacy, achieving 0.94% accuracy.
- The MobileNetV2 model outperformed existing literature methods for mpox image classification.
Conclusions:
- Deep learning, specifically the MobileNetV2 model, offers a promising approach for the early and accurate detection of mpox.
- This AI-driven method can serve as a valuable tool for clinical diagnosis, improving mpox management strategies.
