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- W4295767970 abstract "In the Covid-19 era, it is important to have an edge detector for X-ray (XR) images affected by uncertainties with low computational load but with high performance. So, here, a new version of a well-known fuzzy edge detector, in which a new image fuzzification procedure has been formulated, is proposed. The performance were qualitatively/ quantitatively compared with those obtained by Canny’s edge detector (gold standard for this type of problem). In addition, an evolution of the deep fuzzy-neural model named CovNNet, recently proposed by the authors to discriminate chest XR (CXR) images of patients with Covid-19 pneumonia from images of patients with interstitial pneumonias not related to Covid-19 (No-Covid-19), is presented and referred as to Enhanced-CovNNet (ECovNNet). Here, the generalization ability of it is also improved by introducing a regularization based on dropping out some nodes of the network in a random way. ECovNNet processes input CXR images and the corresponding fuzzy CXR images (processed through the proposed enhanced-fuzzy edge detector) and extracts relevant CXR/fuzzy features, subsequently combined in a single array named CXR and fuzzy features vector. The latter is used as input to an Autoencoder-(AE)-based classifier to perform the binary classification: Covid-19 and No-Covid-19, reporting accuracy rate up to 81%. Finally, the work is completed with some interesting physico-mathematical results." @default.
- W4295767970 created "2022-09-15" @default.
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- W4295767970 date "2022-07-18" @default.
- W4295767970 modified "2023-10-16" @default.
- W4295767970 title "Detection of Covid-19 Pneumonia from chest X-ray images: joint use of ECovNNet and fuzzy distance" @default.
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- W4295767970 doi "https://doi.org/10.1109/fuzz-ieee55066.2022.9882738" @default.
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