Matches in SemOpenAlex for { <https://semopenalex.org/work/W3201728242> ?p ?o ?g. }
Showing items 1 to 89 of
89
with 100 items per page.
- W3201728242 abstract "Chest radiography needs timely diseases diagnosis and reporting of potential findings in the images, as it is an important diagnostic imaging test in medical practice. A crucial step in radiology workflow is the fast, automated, and reliable detection of diseases created on chest radiography. To overcome this issue, an artificial intelligence-based algorithm such as deep learning (DL) are promising methods for automatic and fast diagnosis due to their excellent performance analysis of a wide range of medical images and visual information. This paper surveys the DL methods for lung disease detection from chest X-ray images. The common five attributes surveyed in the articles are data augmentation, transfer learning, types of DL algorithms, types of lung diseases and features used for detection of abnormalities, and types of lung diseases. The presented methods may prove extremely useful for people to ideate their research contributions in this area." @default.
- W3201728242 created "2021-10-11" @default.
- W3201728242 creator A5011664058 @default.
- W3201728242 creator A5029447188 @default.
- W3201728242 creator A5068831226 @default.
- W3201728242 creator A5073031955 @default.
- W3201728242 date "2021-09-26" @default.
- W3201728242 modified "2023-09-26" @default.
- W3201728242 title "Deep Learning Methodologies for Diagnosis of Respiratory Disorders from Chest X-ray Images: A Comparative Study" @default.
- W3201728242 cites W1976863970 @default.
- W3201728242 cites W2136922672 @default.
- W3201728242 cites W2161336914 @default.
- W3201728242 cites W2253590344 @default.
- W3201728242 cites W2342469653 @default.
- W3201728242 cites W2592929672 @default.
- W3201728242 cites W2734760437 @default.
- W3201728242 cites W2788633781 @default.
- W3201728242 cites W2796442970 @default.
- W3201728242 cites W2809598685 @default.
- W3201728242 cites W2810767764 @default.
- W3201728242 cites W2906991531 @default.
- W3201728242 cites W2908763778 @default.
- W3201728242 cites W2924911266 @default.
- W3201728242 cites W2939788146 @default.
- W3201728242 cites W2940612399 @default.
- W3201728242 cites W2954996726 @default.
- W3201728242 cites W2979617155 @default.
- W3201728242 cites W3011149445 @default.
- W3201728242 cites W3040120150 @default.
- W3201728242 cites W3042426630 @default.
- W3201728242 cites W3045625006 @default.
- W3201728242 cites W3085331204 @default.
- W3201728242 cites W3091861943 @default.
- W3201728242 cites W3106437781 @default.
- W3201728242 cites W3162351260 @default.
- W3201728242 cites W4205623122 @default.
- W3201728242 cites W4232097126 @default.
- W3201728242 doi "https://doi.org/10.3390/ioca2021-10900" @default.
- W3201728242 hasPublicationYear "2021" @default.
- W3201728242 type Work @default.
- W3201728242 sameAs 3201728242 @default.
- W3201728242 citedByCount "0" @default.
- W3201728242 crossrefType "proceedings-article" @default.
- W3201728242 hasAuthorship W3201728242A5011664058 @default.
- W3201728242 hasAuthorship W3201728242A5029447188 @default.
- W3201728242 hasAuthorship W3201728242A5068831226 @default.
- W3201728242 hasAuthorship W3201728242A5073031955 @default.
- W3201728242 hasBestOaLocation W32017282421 @default.
- W3201728242 hasConcept C108583219 @default.
- W3201728242 hasConcept C119857082 @default.
- W3201728242 hasConcept C126838900 @default.
- W3201728242 hasConcept C150899416 @default.
- W3201728242 hasConcept C154945302 @default.
- W3201728242 hasConcept C177212765 @default.
- W3201728242 hasConcept C19527891 @default.
- W3201728242 hasConcept C31601959 @default.
- W3201728242 hasConcept C36454342 @default.
- W3201728242 hasConcept C41008148 @default.
- W3201728242 hasConcept C71924100 @default.
- W3201728242 hasConcept C77088390 @default.
- W3201728242 hasConceptScore W3201728242C108583219 @default.
- W3201728242 hasConceptScore W3201728242C119857082 @default.
- W3201728242 hasConceptScore W3201728242C126838900 @default.
- W3201728242 hasConceptScore W3201728242C150899416 @default.
- W3201728242 hasConceptScore W3201728242C154945302 @default.
- W3201728242 hasConceptScore W3201728242C177212765 @default.
- W3201728242 hasConceptScore W3201728242C19527891 @default.
- W3201728242 hasConceptScore W3201728242C31601959 @default.
- W3201728242 hasConceptScore W3201728242C36454342 @default.
- W3201728242 hasConceptScore W3201728242C41008148 @default.
- W3201728242 hasConceptScore W3201728242C71924100 @default.
- W3201728242 hasConceptScore W3201728242C77088390 @default.
- W3201728242 hasLocation W32017282421 @default.
- W3201728242 hasOpenAccess W3201728242 @default.
- W3201728242 hasPrimaryLocation W32017282421 @default.
- W3201728242 hasRelatedWork W2946016983 @default.
- W3201728242 hasRelatedWork W2960456850 @default.
- W3201728242 hasRelatedWork W3163306278 @default.
- W3201728242 hasRelatedWork W4213299466 @default.
- W3201728242 hasRelatedWork W4312200629 @default.
- W3201728242 hasRelatedWork W4312685930 @default.
- W3201728242 hasRelatedWork W4317565044 @default.
- W3201728242 hasRelatedWork W4318834068 @default.
- W3201728242 hasRelatedWork W4318957922 @default.
- W3201728242 hasRelatedWork W4322727400 @default.
- W3201728242 isParatext "false" @default.
- W3201728242 isRetracted "false" @default.
- W3201728242 magId "3201728242" @default.
- W3201728242 workType "article" @default.