Background: The emergence of monkeypox as a global health concern highlights the need for innovative detection methods that improve upon polymerase chain reaction, which is costly, time-consuming, and poses risks of contagion to healthcare personnel.
Purpose: This study proposed a lightweight deep learning framework to enhance monkeypox lesion detection using skin image data.
Methods: Data augmentation and a novel edge enhancement algorithm are applied, employing contrast-limited adaptive histogram equalization and bilateral filters to refine skin images. The framework is tested across six pretrained deep learning models and one novel hybrid deep model, DenseNet121 + ConvNeXt-Tiny (DN-CXT). Performance is evaluated using accuracy, F1-score, and precision, with optimization through Adam, root mean square propagation, and stochastic gradient descent.
Results: The proposed DN-CXT model achieved the highest performance, with a test accuracy of 97%, F1-score of 97%, and precision of 99%. Applied techniques such as DenseNet121, MobileNetV2, InceptionV3, and ConvNeXt-Tiny also showed exceptional results.
Conclusions: The proposed framework significantly advances medical image detection for monkeypox lesions.
Implications: These findings support the integration of artificial intelligence-driven methodologies into monkeypox detection workflows, potentially improving diagnostic efficiency, reducing risks to medical personnel, and enhancing healthcare response to emerging infectious diseases.
Keywords: Monkeypox detection; deep neural networks; edge enhancement; hybrid deep model; skin cancer.
© The Author(s) 2026.