Enhancing Monkeypox Detection with Efficientnet-B5 And Image Augmentation Fusion Technique

Abdullahi Lawal Rukuna; Umar Muhammad Bello; Abdulmalik Abdulsalam; Ahmad Abubakar Yusuf; Dr. Adam Ibrahim Abdullahi; Mustapha Isah; Harisu Aliyu; Danladi Shemang Kage; Sunusi Haruna

doi:10.32628/IJSRST241161119

Authors

Abdullahi Lawal Rukuna Abubakar Tafawa Ballewa University Author
Umar Muhammad Bello Federal University Gusau Author
Abdulmalik Abdulsalam National Open University of Nigeria Author
Ahmad Abubakar Yusuf Federal Polytechnic Bauchi Author
Dr. Adam Ibrahim Abdullahi Sa’adu Zungru University Author
Mustapha Isah Usmanu Danfodiyo University Author
Harisu Aliyu Abubakar Tafawa Ballewa University Author
Danladi Shemang Kage Abubakar Tafawa Ballewa University Author
Sunusi Haruna Abubakar Tafawa Ballewa University Author

DOI:

https://doi.org/10.32628/IJSRST241161119

Keywords:

monkeypox, EfficientNet-B5, augmentation fusion, transfer learning, deep learning

Abstract

The recent surge of monkeypox infections worldwide has underscored the need for rapid, accurate diagnostic tools, particularly in regions with limited access to laboratory-based tests. This study employs deep learning, utilizing a pre-trained efficientNet-B5 model through transfer learning, to classify monkeypox from digital skin lesion images. Data was compiled from Kaggle, web scraping, and hospital records, covering both monkeypox and similar skin conditions such as chickenpox, measles and smallpox. The dataset was preprocessed using advanced augmentation fusion techniques, enhancing image diversity and maintaining diagnostic features critical for the model's efficacy. The efficientNet-B5 model achieved impressive results, demonstrating 99.47% accuracy, 99.19% precision and a recall of 99.72 for monkeypox. These findings suggest that the efficientNet-B5 model, supported by augmentation fusion, can serve as a reliable tool for detecting monkeypox, providing a scalable solution for early identification and public health intervention in resource-constrained settings.

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