Deep Learning-Based Prediction Model for Monkeypox Detection

Devraj Gautam; Payal; Prerna; Surender  Kumar; Kamya  Dhingra

doi:10.65890/race.v2i1.159

Authors

Devraj Gautam Department of Electronics & Communication Engineering, Dr Akhilesh Das Gupta Institute of Professional Studies, GGSIPU, India Author
Payal Department of Electronics & Communication Engineering, Dr Akhilesh Das Gupta Institute of Professional Studies, GGSIPU, India Author
Prerna Department of Electronics & Communication Engineering, Dr Akhilesh Das Gupta Institute of Professional Studies, GGSIPU, India Author
Surender Kumar Department of Electronics & Communication Engineering, Dr Akhilesh Das Gupta Institute of Professional Studies, GGSIPU, India Author
Kamya Dhingra Department of Electronics & Communication Engineering, Dr Akhilesh Das Gupta Institute of Professional Studies, GGSIPU, India Author

DOI:

https://doi.org/10.65890/race.v2i1.159

Keywords:

Monkeypox Detection, Convolutional Neural Network, Image Segmentation, Transfer Learning, MobileNetV2, VGG16, U-Net, Deep Learning

Abstract

Monkeypox is an emerging zoonotic viral disease caused by the Monkeypox virus (MPXV), which gained global attention following the unprecedented 2022 outbreak. The rapid spread of the disease across multiple continents highlighted the urgent need for efficient, scalable, and accessible diagnostic tools. This study proposes a deep learning-based prediction model for the automated detection of Monkeypox from skin lesion images, aimed at supporting early diagnosis, particularly in resource-constrained and rural healthcare settings. The proposed system employs a U-Net-based segmentation framework to isolate infected lesion regions, followed by Convolutional Neural Network (CNN) classifiers trained on a curated dataset of over 300 labelled images encompassing Monkeypox, Chickenpox, and healthy skin samples. To enhance model robustness and generalization, preprocessing techniques including noise reduction, normalization, and data augmentation were systematically applied. Several state-of-the-art transfer learning architectures, including AlexNet, VGG16, ResNet, InceptionNet, and MobileNetV2, were evaluated and benchmarked under uniform training conditions. The model demonstrates promising classification performance and holds significant potential for deployment as a mobile or web-based diagnostic aid, bridging critical gaps in healthcare accessibility worldwide.

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