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dc.contributor.authorDey, S
dc.contributor.authorBhattacharya, R
dc.contributor.authorMalakar, S
dc.contributor.authorMirjalili, S
dc.contributor.authorSarkar, R
dc.date.accessioned2021-07-19T03:52:23Z
dc.date.available2021-07-19T03:52:23Z
dc.date.issued2021
dc.identifier.issn0010-4825
dc.identifier.doi10.1016/j.compbiomed.2021.104585
dc.identifier.urihttp://hdl.handle.net/10072/406114
dc.description.abstractThe COVID-19 outbreak has resulted in a global pandemic and led to more than a million deaths to date. COVID-19 early detection is essential for its mitigation by controlling its spread from infected patients in communities through quarantine. Although vaccination has started, it will take time to reach everyone, especially in developing nations, and computer scientists are striving to come up with competent methods using image analysis. In this work, a classifier ensemble technique is proposed, utilizing Choquet fuzzy integral, wherein convolutional neural network (CNN) based models are used as base classifiers. It classifies chest X-ray images from patients with common Pneumonia, confirmed COVID-19, and healthy lungs. Since there are few samples of COVID-19 cases for training on a standard CNN model from scratch, we use the transfer learning scheme to train the base classifiers, which are InceptionV3, DenseNet121, and VGG19. We utilize the pre-trained CNN models to extract features and classify the chest X-ray images using two dense layers and one softmax layer. After that, we combine the prediction scores of the data from individual models using Choquet fuzzy integral to get the final predicted labels, which is more accurate than the prediction by the individual models. To determine the fuzzy-membership values of each classifier for the application of Choquet fuzzy integral, we use the validation accuracy of each classifier. The proposed method is evaluated on chest X-ray images in publicly available repositories (IEEE and Kaggle datasets). It provides 99.00%, 99.00%, 99.00%, and 99.02% average recall, precision, F-score, and accuracy, respectively. We have also evaluated the performance of the proposed model on an inter-dataset experimental setup, where chest X-ray images from another dataset (CMSC-678-ML-Project GitHub dataset) are fed to our trained model and we have achieved 99.05% test accuracy on this dataset. The results are better than commonly used classifier ensemble methods as well as many state-of-the-art methods.
dc.description.peerreviewedYes
dc.languageEnglish
dc.publisherElsevier BV
dc.relation.ispartofjournalComputers in Biology and Medicine
dc.relation.ispartofvolume135
dc.subject.fieldofresearchInformation and computing sciences
dc.subject.fieldofresearchEngineering
dc.subject.fieldofresearchBiomedical and clinical sciences
dc.subject.fieldofresearchcode46
dc.subject.fieldofresearchcode40
dc.subject.fieldofresearchcode32
dc.subject.keywordsCOVID-19
dc.subject.keywordsChoquet integral
dc.subject.keywordsDeep learning
dc.subject.keywordsEnsemble method
dc.subject.keywordsMachine learning
dc.titleChoquet fuzzy integral-based classifier ensemble technique for COVID-19 detection
dc.typeJournal article
dc.type.descriptionC1 - Articles
dcterms.bibliographicCitationDey, S; Bhattacharya, R; Malakar, S; Mirjalili, S; Sarkar, R, Choquet fuzzy integral-based classifier ensemble technique for COVID-19 detection, Computers in Biology and Medicine, 2021, 135
dcterms.dateAccepted2021-06-12
dc.date.updated2021-07-18T23:16:39Z
gro.hasfulltextNo Full Text
gro.griffith.authorMirjalili, Seyedali


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