Matches in SemOpenAlex for { <https://semopenalex.org/work/W3215775222> ?p ?o ?g. }
Showing items 1 to 88 of
88
with 100 items per page.
- W3215775222 endingPage "16" @default.
- W3215775222 startingPage "1" @default.
- W3215775222 abstract "A method of a Convolutional Neural Networks (CNN) for image classification with image preprocessing and hyperparameters tuning was proposed. The method aims at increasing the predictive performance for COVID-19 diagnosis while more complex model architecture. Firstly, the CNN model includes four similar convolutional layers followed by a flattening and two dense layers. This work proposes a less complex solution based on simply classifying 2D-slices of Computed Tomography scans. Despite the simplicity in architecture, the proposed CNN model showed improved quantitative results exceeding state-of-the-art when predicting slice cases. The results were achieved on the annotated CT slices of the COV-19-CT-DB dataset. Secondly, the original dataset was processed via anatomy-relevant masking of slice, removing none-representative slices from the CT volume, and hyperparameters tuning. For slice processing, a fixed-sized rectangular area was used for cropping an anatomy-relevant region-of-interest in the images, and a threshold based on the number of white pixels in binarized slices was employed to remove none-representative slices from the 3D-CT scans. The CNN model with a learning rate schedule and an exponential decay and slice flipping techniques was deployed on the processed slices. The proposed method was used to make predictions on the 2D slices and for final diagnosis at patient level, majority voting was applied on the slices of each CT scan to take the diagnosis. The macro F1 score of the proposed method well-exceeded the baseline approach and other alternatives on the validation set as well as on a test partition of previously unseen images from COV-19CT-DB dataset." @default.
- W3215775222 created "2021-12-06" @default.
- W3215775222 creator A5006754641 @default.
- W3215775222 creator A5020999017 @default.
- W3215775222 date "2023-06-02" @default.
- W3215775222 modified "2023-09-27" @default.
- W3215775222 title "Deep learning-based automated COVID-19 classification from computed tomography images" @default.
- W3215775222 cites W1604924365 @default.
- W3215775222 cites W2767195103 @default.
- W3215775222 cites W2769215776 @default.
- W3215775222 cites W2962858109 @default.
- W3215775222 cites W2971923510 @default.
- W3215775222 cites W3010496206 @default.
- W3215775222 cites W3013174866 @default.
- W3215775222 cites W3013633552 @default.
- W3215775222 cites W3014524604 @default.
- W3215775222 cites W3016653598 @default.
- W3215775222 cites W3025953162 @default.
- W3215775222 cites W3027763298 @default.
- W3215775222 cites W3030621456 @default.
- W3215775222 cites W3035825675 @default.
- W3215775222 cites W3080758677 @default.
- W3215775222 cites W3089290459 @default.
- W3215775222 cites W3103635657 @default.
- W3215775222 cites W3109022689 @default.
- W3215775222 cites W3121891683 @default.
- W3215775222 cites W3131326546 @default.
- W3215775222 cites W3132764260 @default.
- W3215775222 cites W3133765315 @default.
- W3215775222 cites W3154692872 @default.
- W3215775222 cites W3182345052 @default.
- W3215775222 cites W3187743275 @default.
- W3215775222 cites W3208040116 @default.
- W3215775222 cites W4205249885 @default.
- W3215775222 cites W4205761504 @default.
- W3215775222 cites W4211225657 @default.
- W3215775222 cites W4220782498 @default.
- W3215775222 cites W4220804187 @default.
- W3215775222 cites W4311219348 @default.
- W3215775222 doi "https://doi.org/10.1080/21681163.2023.2219765" @default.
- W3215775222 hasPublicationYear "2023" @default.
- W3215775222 type Work @default.
- W3215775222 sameAs 3215775222 @default.
- W3215775222 citedByCount "0" @default.
- W3215775222 crossrefType "journal-article" @default.
- W3215775222 hasAuthorship W3215775222A5006754641 @default.
- W3215775222 hasAuthorship W3215775222A5020999017 @default.
- W3215775222 hasBestOaLocation W32157752222 @default.
- W3215775222 hasConcept C108583219 @default.
- W3215775222 hasConcept C153180895 @default.
- W3215775222 hasConcept C154945302 @default.
- W3215775222 hasConcept C160633673 @default.
- W3215775222 hasConcept C31972630 @default.
- W3215775222 hasConcept C34736171 @default.
- W3215775222 hasConcept C41008148 @default.
- W3215775222 hasConcept C58489278 @default.
- W3215775222 hasConcept C81363708 @default.
- W3215775222 hasConcept C8642999 @default.
- W3215775222 hasConceptScore W3215775222C108583219 @default.
- W3215775222 hasConceptScore W3215775222C153180895 @default.
- W3215775222 hasConceptScore W3215775222C154945302 @default.
- W3215775222 hasConceptScore W3215775222C160633673 @default.
- W3215775222 hasConceptScore W3215775222C31972630 @default.
- W3215775222 hasConceptScore W3215775222C34736171 @default.
- W3215775222 hasConceptScore W3215775222C41008148 @default.
- W3215775222 hasConceptScore W3215775222C58489278 @default.
- W3215775222 hasConceptScore W3215775222C81363708 @default.
- W3215775222 hasConceptScore W3215775222C8642999 @default.
- W3215775222 hasLocation W32157752221 @default.
- W3215775222 hasLocation W32157752222 @default.
- W3215775222 hasOpenAccess W3215775222 @default.
- W3215775222 hasPrimaryLocation W32157752221 @default.
- W3215775222 hasRelatedWork W2732542196 @default.
- W3215775222 hasRelatedWork W2738221750 @default.
- W3215775222 hasRelatedWork W2977314777 @default.
- W3215775222 hasRelatedWork W2997155179 @default.
- W3215775222 hasRelatedWork W3130227562 @default.
- W3215775222 hasRelatedWork W3144574764 @default.
- W3215775222 hasRelatedWork W3156786002 @default.
- W3215775222 hasRelatedWork W3159690776 @default.
- W3215775222 hasRelatedWork W4304182771 @default.
- W3215775222 hasRelatedWork W564581980 @default.
- W3215775222 isParatext "false" @default.
- W3215775222 isRetracted "false" @default.
- W3215775222 magId "3215775222" @default.
- W3215775222 workType "article" @default.