论文标题
使用预训练的深度学习方法检测蒙基氧基病毒
Monkeypox virus detection using pre-trained deep learning-based approaches
论文作者
论文摘要
随着世界各地的COVID-19病毒感染的下降，Monkeypox病毒正在缓慢地出现。人们害怕它，以为它会像19岁一样成为大流行。因此，在广泛的社区传播之前，至关重要的是检测到它们。基于AI的检测可以帮助他们在早期识别它们。在本文中，我们旨在比较13种不同的预训练的深度学习（DL）模型，以检测蒙基氧基病毒。为此，我们最初对它们都添加了通用自定义层，并使用四个完善的措施来分析结果：精度，召回，F1得分和准确性。在确定了表现最佳的DL模型之后，我们将它们整合，以通过对从中获得的概率输出进行多数投票来提高整体绩效。我们在公开可用的数据集上执行实验，该数据集的平均精度，召回，F1得分和精度为85.44 \％，85.47 \％，85.40 \％和87.13 \％，分别在我们提出的集合方法的帮助下。这些令人鼓舞的结果胜过最先进的方法，这表明所提出的方法适用于卫生从业人员进行大规模筛查。
Monkeypox virus is emerging slowly with the decline of COVID-19 virus infections around the world. People are afraid of it, thinking that it would appear as a pandemic like COVID-19. As such, it is crucial to detect them earlier before widespread community transmission. AI-based detection could help identify them at the early stage. In this paper, we aim to compare 13 different pre-trained deep learning (DL) models for the Monkeypox virus detection. For this, we initially fine-tune them with the addition of universal custom layers for all of them and analyse the results using four well-established measures: Precision, Recall, F1-score, and Accuracy. After the identification of the best-performing DL models, we ensemble them to improve the overall performance using a majority voting over the probabilistic outputs obtained from them. We perform our experiments on a publicly available dataset, which results in average Precision, Recall, F1-score, and Accuracy of 85.44\%, 85.47\%, 85.40\%, and 87.13\%, respectively with the help of our proposed ensemble approach. These encouraging results, which outperform the state-of-the-art methods, suggest that the proposed approach is applicable to health practitioners for mass screening.
