About: Exploration of Interpretability Techniques for Deep COVID-19 Classification using Chest X-ray Images

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About: Exploration of Interpretability Techniques for Deep COVID-19 Classification using Chest X-ray Images Goto Sponge NotDistinct Permalink

An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

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type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	Exploration of Interpretability Techniques for Deep COVID-19 Classification using Chest X-ray Images
Creator	Chatterjee, Soumick Ghosh, Suhita Khatun, Rupali Nürnberger, Andreas Radeva, Petia »more»
source	ArXiv
abstract	The outbreak of COVID-19 has shocked the entire world with its fairly rapid spread and has challenged different sectors. One of the most effective ways to limit its spread is the early and accurate diagnosis of infected patients. Medical imaging such as X-ray and Computed Tomography (CT) combined with the potential of Artificial Intelligence (AI) plays an essential role in supporting the medical staff in the diagnosis process. Thereby, the use of five different deep learning models (ResNet18, ResNet34, InceptionV3, InceptionResNetV2, and DenseNet161) and their Ensemble have been used in this paper, to classify COVID-19, pneumoni{/ae} and healthy subjects using Chest X-Ray. Multi-label classification was performed to predict multiple pathologies for each patient, if present. Foremost, the interpretability of each of the networks was thoroughly studied using techniques like occlusion, saliency, input X gradient, guided backpropagation, integrated gradients, and DeepLIFT. The mean Micro-F1 score of the models for COVID-19 classifications ranges from 0.66 to 0.875, and is 0.89 for the Ensemble of the network models. The qualitative results depicted the ResNets to be the most interpretable model.
has issue date	2020-06-03(xsd:dateTime)
has license	arxiv
sha1sum (hex)	bdaa6db473410b107e69ae3d364b2ce4de79b427
resource representing a document's title	Exploration of Interpretability Techniques for Deep COVID-19 Classification using Chest X-ray Images
resource representing a document's body	covid:bdaa6db473410b107e69ae3d364b2ce4de79b427#body_text
is schema:about of	named entity 'studied' named entity 'gradients' named entity 'diagnosis' covid:arg/bdaa6db473410b107e69ae3d364b2ce4de79b427 named entity 'rapid' »more»

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