Matches in SemOpenAlex for { <https://semopenalex.org/work/W4283067544> ?p ?o ?g. }
Showing items 1 to 97 of
97
with 100 items per page.
- W4283067544 endingPage "12" @default.
- W4283067544 startingPage "1" @default.
- W4283067544 abstract "The 2019 coronavirus pandemic (COVID-19) struck without warning, and existing medical screening and clinical management systems were unprepared, causing a high fatality rate. Given the virus’s ongoing evolution, there is still a potential for reemergence; earlier weak preparedness will not be accepted in such a situation. Therefore, it is vital to understand and rectify past diagnostic work’s flaws. RT-PCR and antigen tests, both widely used, have experienced problems in the past. They either were too sluggish or produced an excessive number of false negatives. Another issue was a lack of test kits. As a result, chest X-ray image-based disease classification has emerged. However, managing a variety of chest X-ray pictures for COVID-19 and pneumonia patients is complicated and error-prone. As a result, the only way to improve the current diagnosis is to apply deep learning algorithms that learn from radiography pictures and anticipate COVID-19 development. We constructed our own convolutional neural network (CNN) by incorporating transfer learning from the most popular ResNet, VGG, and InceptionNet models. The endeavor necessitated the creation of a sizable dataset that accurately depicted the patient population. Before importing the model, the images were enhanced to remove artifacts caused by noise, motion, or blurring that could impair the detection of infection. Preprocessing has a substantial impact on the model’s accuracy. The results indicated that the VGG16 architecture, with a detection accuracy of 95.29%, is optimal for COVID-19 identification from X-ray images. Furthermore, most generated models outperformed current state-of-the-art research in the same field." @default.
- W4283067544 created "2022-06-19" @default.
- W4283067544 creator A5006888935 @default.
- W4283067544 creator A5044812157 @default.
- W4283067544 creator A5046208589 @default.
- W4283067544 creator A5087175697 @default.
- W4283067544 date "2022-06-17" @default.
- W4283067544 modified "2023-09-25" @default.
- W4283067544 title "Analysis of Chest X-Ray Images for the Recognition of COVID-19 Symptoms Using CNN" @default.
- W4283067544 cites W2044097773 @default.
- W4283067544 cites W2194775991 @default.
- W4283067544 cites W2911410812 @default.
- W4283067544 cites W2948666538 @default.
- W4283067544 cites W2964350391 @default.
- W4283067544 cites W3002108456 @default.
- W4283067544 cites W3007497549 @default.
- W4283067544 cites W3007764760 @default.
- W4283067544 cites W3010604545 @default.
- W4283067544 cites W3013277995 @default.
- W4283067544 cites W3016488464 @default.
- W4283067544 cites W3024801014 @default.
- W4283067544 cites W3025953162 @default.
- W4283067544 cites W3028070348 @default.
- W4283067544 cites W3035986928 @default.
- W4283067544 cites W3045460727 @default.
- W4283067544 cites W3049131298 @default.
- W4283067544 cites W3083972167 @default.
- W4283067544 cites W3088546648 @default.
- W4283067544 cites W3095615238 @default.
- W4283067544 cites W3105081694 @default.
- W4283067544 cites W3132499588 @default.
- W4283067544 cites W3135057764 @default.
- W4283067544 cites W3135243128 @default.
- W4283067544 cites W3165261737 @default.
- W4283067544 cites W4205392594 @default.
- W4283067544 cites W4214767566 @default.
- W4283067544 cites W4280493037 @default.
- W4283067544 doi "https://doi.org/10.1155/2022/3237361" @default.
- W4283067544 hasPublicationYear "2022" @default.
- W4283067544 type Work @default.
- W4283067544 citedByCount "0" @default.
- W4283067544 crossrefType "journal-article" @default.
- W4283067544 hasAuthorship W4283067544A5006888935 @default.
- W4283067544 hasAuthorship W4283067544A5044812157 @default.
- W4283067544 hasAuthorship W4283067544A5046208589 @default.
- W4283067544 hasAuthorship W4283067544A5087175697 @default.
- W4283067544 hasBestOaLocation W42830675441 @default.
- W4283067544 hasConcept C108583219 @default.
- W4283067544 hasConcept C119857082 @default.
- W4283067544 hasConcept C142724271 @default.
- W4283067544 hasConcept C150899416 @default.
- W4283067544 hasConcept C153180895 @default.
- W4283067544 hasConcept C154945302 @default.
- W4283067544 hasConcept C2779134260 @default.
- W4283067544 hasConcept C2908647359 @default.
- W4283067544 hasConcept C3008058167 @default.
- W4283067544 hasConcept C34736171 @default.
- W4283067544 hasConcept C41008148 @default.
- W4283067544 hasConcept C524204448 @default.
- W4283067544 hasConcept C71924100 @default.
- W4283067544 hasConcept C81363708 @default.
- W4283067544 hasConcept C99454951 @default.
- W4283067544 hasConceptScore W4283067544C108583219 @default.
- W4283067544 hasConceptScore W4283067544C119857082 @default.
- W4283067544 hasConceptScore W4283067544C142724271 @default.
- W4283067544 hasConceptScore W4283067544C150899416 @default.
- W4283067544 hasConceptScore W4283067544C153180895 @default.
- W4283067544 hasConceptScore W4283067544C154945302 @default.
- W4283067544 hasConceptScore W4283067544C2779134260 @default.
- W4283067544 hasConceptScore W4283067544C2908647359 @default.
- W4283067544 hasConceptScore W4283067544C3008058167 @default.
- W4283067544 hasConceptScore W4283067544C34736171 @default.
- W4283067544 hasConceptScore W4283067544C41008148 @default.
- W4283067544 hasConceptScore W4283067544C524204448 @default.
- W4283067544 hasConceptScore W4283067544C71924100 @default.
- W4283067544 hasConceptScore W4283067544C81363708 @default.
- W4283067544 hasConceptScore W4283067544C99454951 @default.
- W4283067544 hasFunder F4320321145 @default.
- W4283067544 hasLocation W42830675441 @default.
- W4283067544 hasOpenAccess W4283067544 @default.
- W4283067544 hasPrimaryLocation W42830675441 @default.
- W4283067544 hasRelatedWork W3018421652 @default.
- W4283067544 hasRelatedWork W3021430260 @default.
- W4283067544 hasRelatedWork W3091976719 @default.
- W4283067544 hasRelatedWork W3192840557 @default.
- W4283067544 hasRelatedWork W3195938642 @default.
- W4283067544 hasRelatedWork W4220996320 @default.
- W4283067544 hasRelatedWork W4226119185 @default.
- W4283067544 hasRelatedWork W4285149559 @default.
- W4283067544 hasRelatedWork W4312200629 @default.
- W4283067544 hasRelatedWork W4382286161 @default.
- W4283067544 hasVolume "2022" @default.
- W4283067544 isParatext "false" @default.
- W4283067544 isRetracted "false" @default.
- W4283067544 workType "article" @default.