Transfer learning and Local interpretable model agnostic based visual approach in Monkeypox Disease Detection and Classification: A Deep Learning insights
This work addresses early diagnosis of Monkeypox, a potential pandemic threat, but is incremental as it applies existing transfer learning methods to a new disease dataset.
The paper tackles Monkeypox disease detection and classification by modifying six deep learning models using transfer learning, achieving accuracies from 93% to 99% with InceptionResNetV2 and MobileNetV2 performing best, and uses LIME for interpretability.
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.