DEEP LEARNING-BASED ADAPTIVE ENSEMBLE LEARNING MODEL FOR CLASSIFICATION OF MONKEYPOX DISEASE

Abstract

Monkeypox a viral disease resembling smallpox often transmitted via animal contact or human-to-human transmission. Symptoms include fever, rash, and respiratory issues. Healthcare experts initially may confuse it with chickenpox or measles due to its rarity, but swollen lymph nodes typically distinguish it. Diagnosis involves tissue sampling and polymerase chain reaction (PCR) testing, although PCR tests have limitations like time consumption and false negatives. Deep learning-based detection offers advantages over PCR, including reduced risk of exposure, quicker results, and improved accuracy. In this study, a novel adaptive ensemble learning (AEL)-based model for monkeypox diagnosis is proposed. This proposed ensemble learning model aims to enhance diagnosis accuracy by combining different deep learning models, leveraging an adaptive approach for model combination. Experimental studies using MSLD and MSID datasets show promising results, with ensemble models achieving high accuracy, precision, recall, and F1 scores. The ResNet101+VGG16 (92.46% accuracy, 92.75% precision, 93.22% recall, and 92.98% F1 score) ensemble model performs best for MSLD, while DenseNet121+Xception (97.58% accuracy, 96.57% precision, 95.74% recall, and 96.14% F1 score) excels for MSID. In addition, the proposed AEL model outperforms previous studies using the same datasets, showcasing its potential for improved monkeypox diagnosis.