FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3135057764> ?p ?o ?g. }

• W3135057764 endingPage "104319" @default.
• W3135057764 startingPage "104319" @default.
• W3135057764 abstract "Computer-aided diagnosis for the reliable and fast detection of coronavirus disease (COVID-19) has become a necessity to prevent the spread of the virus during the pandemic to ease the burden on the healthcare system. Chest X-ray (CXR) imaging has several advantages over other imaging and detection techniques. Numerous works have been reported on COVID-19 detection from a smaller set of original X-ray images. However, the effect of image enhancement and lung segmentation of a large dataset in COVID-19 detection was not reported in the literature. We have compiled a large X-ray dataset (COVQU) consisting of 18,479 CXR images with 8851 normal, 6012 non-COVID lung infections, and 3616 COVID-19 CXR images and their corresponding ground truth lung masks. To the best of our knowledge, this is the largest public COVID positive database and the lung masks. Five different image enhancement techniques: histogram equalization (HE), contrast limited adaptive histogram equalization (CLAHE), image complement, gamma correction, and balance contrast enhancement technique (BCET) were used to investigate the effect of image enhancement techniques on COVID-19 detection. A novel U-Net model was proposed and compared with the standard U-Net model for lung segmentation. Six different pre-trained Convolutional Neural Networks (CNNs) (ResNet18, ResNet50, ResNet101, InceptionV3, DenseNet201, and ChexNet) and a shallow CNN model were investigated on the plain and segmented lung CXR images. The novel U-Net model showed an accuracy, Intersection over Union (IoU), and Dice coefficient of 98.63%, 94.3%, and 96.94%, respectively for lung segmentation. The gamma correction-based enhancement technique outperforms other techniques in detecting COVID-19 from the plain and the segmented lung CXR images. Classification performance from plain CXR images is slightly better than the segmented lung CXR images; however, the reliability of network performance is significantly improved for the segmented lung images, which was observed using the visualization technique. The accuracy, precision, sensitivity, F1-score, and specificity were 95.11%, 94.55%, 94.56%, 94.53%, and 95.59% respectively for the segmented lung images. The proposed approach with very reliable and comparable performance will boost the fast and robust COVID-19 detection using chest X-ray images." @default.
• W3135057764 created "2021-03-15" @default.
• W3135057764 creator A5019712625 @default.
• W3135057764 creator A5037399253 @default.
• W3135057764 creator A5038491744 @default.
• W3135057764 creator A5040188609 @default.
• W3135057764 creator A5046314264 @default.
• W3135057764 creator A5047979812 @default.
• W3135057764 creator A5049954961 @default.
• W3135057764 creator A5058154727 @default.
• W3135057764 creator A5063670409 @default.
• W3135057764 creator A5073146008 @default.
• W3135057764 creator A5086043405 @default.
• W3135057764 date "2021-05-01" @default.
• W3135057764 modified "2023-10-10" @default.
• W3135057764 title "Exploring the effect of image enhancement techniques on COVID-19 detection using chest X-ray images" @default.
• W3135057764 cites W2136118810 @default.
• W3135057764 cites W2165698076 @default.
• W3135057764 cites W2346062110 @default.
• W3135057764 cites W2560476520 @default.
• W3135057764 cites W2770908349 @default.
• W3135057764 cites W2884436604 @default.
• W3135057764 cites W2888358068 @default.
• W3135057764 cites W2912250162 @default.
• W3135057764 cites W2963214037 @default.
• W3135057764 cites W3004442222 @default.
• W3135057764 cites W3013277995 @default.
• W3135057764 cites W3013601031 @default.
• W3135057764 cites W3015671971 @default.
• W3135057764 cites W3017309755 @default.
• W3135057764 cites W3017771577 @default.
• W3135057764 cites W3025063365 @default.
• W3135057764 cites W3026888299 @default.
• W3135057764 cites W3033616466 @default.
• W3135057764 cites W3045460727 @default.
• W3135057764 cites W3045801508 @default.
• W3135057764 cites W3049757379 @default.
• W3135057764 cites W3064374686 @default.
• W3135057764 cites W3082631915 @default.
• W3135057764 cites W3083753334 @default.
• W3135057764 cites W3086039674 @default.
• W3135057764 cites W3091787675 @default.
• W3135057764 cites W3091978650 @default.
• W3135057764 cites W3105081694 @default.
• W3135057764 cites W3147450857 @default.
• W3135057764 cites W3155103849 @default.
• W3135057764 cites W3155171916 @default.
• W3135057764 cites W3165429597 @default.
• W3135057764 cites W3196526450 @default.
• W3135057764 doi "https://doi.org/10.1016/j.compbiomed.2021.104319" @default.
• W3135057764 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/7946571" @default.
• W3135057764 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/33799220" @default.
• W3135057764 hasPublicationYear "2021" @default.
• W3135057764 type Work @default.
• W3135057764 sameAs 3135057764 @default.
• W3135057764 citedByCount "420" @default.
• W3135057764 countsByYear W31350577642020 @default.
• W3135057764 countsByYear W31350577642021 @default.
• W3135057764 countsByYear W31350577642022 @default.
• W3135057764 countsByYear W31350577642023 @default.
• W3135057764 crossrefType "journal-article" @default.
• W3135057764 hasAuthorship W3135057764A5019712625 @default.
• W3135057764 hasAuthorship W3135057764A5037399253 @default.
• W3135057764 hasAuthorship W3135057764A5038491744 @default.
• W3135057764 hasAuthorship W3135057764A5040188609 @default.
• W3135057764 hasAuthorship W3135057764A5046314264 @default.
• W3135057764 hasAuthorship W3135057764A5047979812 @default.
• W3135057764 hasAuthorship W3135057764A5049954961 @default.
• W3135057764 hasAuthorship W3135057764A5058154727 @default.
• W3135057764 hasAuthorship W3135057764A5063670409 @default.
• W3135057764 hasAuthorship W3135057764A5073146008 @default.
• W3135057764 hasAuthorship W3135057764A5086043405 @default.
• W3135057764 hasBestOaLocation W31350577641 @default.
• W3135057764 hasConcept C115961682 @default.
• W3135057764 hasConcept C124504099 @default.
• W3135057764 hasConcept C136943445 @default.
• W3135057764 hasConcept C142724271 @default.
• W3135057764 hasConcept C146849305 @default.
• W3135057764 hasConcept C153180895 @default.
• W3135057764 hasConcept C154945302 @default.
• W3135057764 hasConcept C163892561 @default.
• W3135057764 hasConcept C2779134260 @default.
• W3135057764 hasConcept C3008058167 @default.
• W3135057764 hasConcept C30387639 @default.
• W3135057764 hasConcept C31972630 @default.
• W3135057764 hasConcept C41008148 @default.
• W3135057764 hasConcept C524204448 @default.
• W3135057764 hasConcept C71924100 @default.
• W3135057764 hasConcept C81363708 @default.
• W3135057764 hasConcept C89600930 @default.
• W3135057764 hasConcept C9417928 @default.
• W3135057764 hasConceptScore W3135057764C115961682 @default.
• W3135057764 hasConceptScore W3135057764C124504099 @default.
• W3135057764 hasConceptScore W3135057764C136943445 @default.
• W3135057764 hasConceptScore W3135057764C142724271 @default.
• W3135057764 hasConceptScore W3135057764C146849305 @default.
• W3135057764 hasConceptScore W3135057764C153180895 @default.
• W3135057764 hasConceptScore W3135057764C154945302 @default.

• next