论文标题
转移学习和局部可解释的模型基于不可知论的视觉方法在Monkeypox疾病检测和分类中：深度学习见解
Transfer learning and Local interpretable model agnostic based visual approach in Monkeypox Disease Detection and Classification: A Deep Learning insights
论文作者
论文摘要
当世界仍在与2019年冠状病毒疾病作斗争时，各个国家的最新发展构成了全球大流行威胁（Covid-19）。在黎明时，需要认真解决猴蛋白酶疾病的缓慢而稳定的传播。多年来，基于深度学习（DL）的疾病预测通过提供早期，廉价和负担得起的诊断设施来表现出真正的潜力。考虑到这一机会，我们进行了两项研究，在其中修改并测试了六种不同的深度学习模型-VGG16，InceptionResnetv2，Resnet50，Resnet101，MobilenetV2和VGG19使用转移学习方法。我们的初步计算结果表明，提出的修改后的InceptionResnetv2和MobilenEtv2模型的表现最好从93％到99％。最近的学术工作增强了我们的发现，这些工作表明使用转移学习方法构建多种疾病诊断模型的表现得到改善。最后，我们进一步使用局部可解释的模型敏捷解释（Lime）来解释我们的模型预测，该解释在识别表征蒙基匹替疾病发作的重要特征方面起着至关重要的作用。
The recent development of Monkeypox disease among various nations poses a global pandemic threat when the world is still fighting Coronavirus Disease-2019 (COVID-19). At its dawn, the slow and steady transmission of Monkeypox disease among individuals needs to be addressed seriously. Over the years, Deep learning (DL) based disease prediction has demonstrated true potential by providing early, cheap, and affordable diagnosis facilities. Considering this opportunity, we have conducted two studies where we modified and tested six distinct deep learning models-VGG16, InceptionResNetV2, ResNet50, ResNet101, MobileNetV2, and VGG19-using transfer learning approaches. Our preliminary computational results show that the proposed modified InceptionResNetV2 and MobileNetV2 models perform best by achieving an accuracy ranging from 93% to 99%. Our findings are reinforced by recent academic work that demonstrates improved performance in constructing multiple disease diagnosis models using transfer learning approaches. Lastly, we further explain our model prediction using Local Interpretable Model-Agnostic Explanations (LIME), which play an essential role in identifying important features that characterize the onset of Monkeypox disease.
