Related papers: Transfer learning and Local interpretable model agnostic based visual approach in Monkeypox Disease Detection and Classification: A Deep Learning insights

Transfer learning and Local interpretable model agnostic based visual approach in Monkeypox Disease Detection and Classification: A Deep Learning insights

URL: http://arxiv.org/abs/2211.05633v1
Date: Tue, 1 Nov 2022 18:07:34 GMT
Title: Transfer learning and Local interpretable model agnostic based visual approach in Monkeypox Disease Detection and Classification: A Deep Learning insights
Authors: Md Manjurul Ahsan, Tareque Abu Abdullah, Md Shahin Ali, Fatematuj Jahora, Md Khairul Islam, Amin G. Alhashim, Kishor Datta Gupta
Abstract summary: The recent development of Monkeypox disease poses a global pandemic threat when the world is still fighting Coronavirus Disease 2019 (COVID-19). 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%.
Score: 0.0
License: http://creativecommons.org/licenses/by/4.0/
Abstract: 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.

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