article · Biomimetics
Monkeypox infection is associated with the development of distinctive skin lesions, creating a need for rapid diagnostic tools during disease outbreaks. To address this, a diagnostic framework combines deep learning with metaheuristic optimisation to detect monkeypox from lesion presentations. Deep transfer learning through the GoogLeNet network extracts key visual features from images. A binary implementation of the dipper throated optimisation algorithm selects the most relevant features, reducing data complexity. These chosen features are then categorised using a decision tree classifier, whose parameters are refined using a continuous version of the same optimisation algorithm. Across performance evaluations, the system achieved an overall accuracy of 94.35 per cent, alongside an F1-score of 0.92, sensitivity of 0.95, and specificity of 0.61. Statistical assessments confirmed the performance distinction of this diagnostic approach over alternative methods.
Rapid and precise identification of monkeypox from skin lesions is vital for controlling outbreaks and reducing public anxiety following epidemics. By combining automated feature extraction with nature-inspired optimisation algorithms, this method demonstrates how computational tools can screen visual symptoms quickly and assist healthcare systems during health emergencies.
This work could enable software tools for automated monkeypox screening based on clinical images of skin lesions, potentially supporting medical professionals during diagnostic triage. The approach remains early-stage research, as it has been developed and evaluated computationally against alternative models without validation in real-world clinical or field settings.
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The virus that causes monkeypox has been observed in Africa for several years, and it has been linked to the development of skin lesions. Public panic and anxiety have resulted from the deadly repercussions of virus infections following the COVID-19 pandemic. Rapid detection approaches are crucial since COVID-19 has reached a pandemic level. This study’s overarching goal is to use metaheuristic optimization to boost the performance of feature selection and classification methods to identify skin lesions as indicators of monkeypox in the event of a pandemic. Deep learning and transfer learning approaches are used to extract the necessary features. The GoogLeNet network is the deep learning framework used for feature extraction. In addition, a binary implementation of the dipper throated optimization (DTO) algorithm is used for feature selection. The decision tree classifier is then used to label the selected set of features. The decision tree classifier is optimized using the continuous version of the DTO algorithm to improve the classification accuracy. Various evaluation methods are used to compare and contrast the proposed approach and the other competing methods using the following metrics: accuracy, sensitivity, specificity, p-Value, N-Value, and F1-score. Through feature selection and a decision tree classifier, the following results are achieved using the proposed approach; F1-score of 0.92, sensitivity of 0.95, specificity of 0.61, p-Value of 0.89, and N-Value of 0.79. The overall accuracy of the proposed methodology after optimizing the parameters of the decision tree classifier is 94.35%. Furthermore, the analysis of variation (ANOVA) and Wilcoxon signed rank test have been applied to the results to investigate the statistical distinction between the proposed methodology and the alternatives. This comparison verified the uniqueness and importance of the proposed approach to Monkeypox case detection.
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DOI: 10.3390/biomimetics8030313
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