Matches in SemOpenAlex for { <https://semopenalex.org/work/W4377700912> ?p ?o ?g. }
Showing items 1 to 96 of
96
with 100 items per page.
- W4377700912 abstract "In the medical sector, even a small advancement or alteration to current methods can have a major effect on the delivery of crucial health benefit. A large amount of patients can be benefited with the aid of machine learning algorithms and applications. Dental specialist may not be able to investigate and understand a lot of information due to manpower restrictions. As studying dental data is necessary in clinical image analysis, This approach becomes essential. Some different problems with human research is that it’s far greater at risk of errors because of elements like stress, low understanding and shortage of experience. With the help of machine learning ,several efficient diagnostic software and tools can be improved and developed. This in return helps doctors and dental staffs to diagnose in a better way. This study makes a valuable contribution in the dental sector with a different kind of pipe-lined technique by employing ResNet Architecture. The method successfully distinguishes between primary molars and premolars in X-ray images as upper teeth and lower teeth after being trained on a dataset of 200 dental X-ray images. For classification, we employ ResNet Architecture out of various Convolution Neural Network Architectures that have been discovered. Using the analysis of our research on various models, the ResNet model performed better than the other models. We come to the conclusion that the ResNet model generalizes accurately to earlier unseen data and can help in the diagnosis of a various dental conditions given a data set and raw X-ray images and has an accuracy of 83%." @default.
- W4377700912 created "2023-05-24" @default.
- W4377700912 creator A5007347603 @default.
- W4377700912 creator A5026029085 @default.
- W4377700912 creator A5040214339 @default.
- W4377700912 creator A5081038431 @default.
- W4377700912 creator A5092001083 @default.
- W4377700912 creator A5092001084 @default.
- W4377700912 creator A5092001085 @default.
- W4377700912 creator A5092001086 @default.
- W4377700912 date "2023-04-07" @default.
- W4377700912 modified "2023-09-27" @default.
- W4377700912 title "Intra-Oral Periapical Dental Classification using Convolution Neural Network" @default.
- W4377700912 cites W2253429366 @default.
- W4377700912 cites W2533800772 @default.
- W4377700912 cites W2592929672 @default.
- W4377700912 cites W2621007086 @default.
- W4377700912 cites W2788633781 @default.
- W4377700912 cites W2807436399 @default.
- W4377700912 cites W2899380081 @default.
- W4377700912 cites W2916845318 @default.
- W4377700912 cites W2967871842 @default.
- W4377700912 cites W2969790209 @default.
- W4377700912 cites W2992530986 @default.
- W4377700912 cites W3003544448 @default.
- W4377700912 cites W3016927601 @default.
- W4377700912 cites W3094199530 @default.
- W4377700912 cites W3108776516 @default.
- W4377700912 cites W3129469040 @default.
- W4377700912 doi "https://doi.org/10.1109/i2ct57861.2023.10126482" @default.
- W4377700912 hasPublicationYear "2023" @default.
- W4377700912 type Work @default.
- W4377700912 citedByCount "0" @default.
- W4377700912 crossrefType "proceedings-article" @default.
- W4377700912 hasAuthorship W4377700912A5007347603 @default.
- W4377700912 hasAuthorship W4377700912A5026029085 @default.
- W4377700912 hasAuthorship W4377700912A5040214339 @default.
- W4377700912 hasAuthorship W4377700912A5081038431 @default.
- W4377700912 hasAuthorship W4377700912A5092001083 @default.
- W4377700912 hasAuthorship W4377700912A5092001084 @default.
- W4377700912 hasAuthorship W4377700912A5092001085 @default.
- W4377700912 hasAuthorship W4377700912A5092001086 @default.
- W4377700912 hasConcept C115961682 @default.
- W4377700912 hasConcept C119857082 @default.
- W4377700912 hasConcept C123657996 @default.
- W4377700912 hasConcept C138885662 @default.
- W4377700912 hasConcept C142362112 @default.
- W4377700912 hasConcept C153180895 @default.
- W4377700912 hasConcept C153349607 @default.
- W4377700912 hasConcept C154945302 @default.
- W4377700912 hasConcept C177264268 @default.
- W4377700912 hasConcept C194051981 @default.
- W4377700912 hasConcept C199360897 @default.
- W4377700912 hasConcept C2777904410 @default.
- W4377700912 hasConcept C2778137410 @default.
- W4377700912 hasConcept C2944601119 @default.
- W4377700912 hasConcept C41008148 @default.
- W4377700912 hasConcept C41895202 @default.
- W4377700912 hasConcept C45347329 @default.
- W4377700912 hasConcept C50644808 @default.
- W4377700912 hasConcept C75294576 @default.
- W4377700912 hasConceptScore W4377700912C115961682 @default.
- W4377700912 hasConceptScore W4377700912C119857082 @default.
- W4377700912 hasConceptScore W4377700912C123657996 @default.
- W4377700912 hasConceptScore W4377700912C138885662 @default.
- W4377700912 hasConceptScore W4377700912C142362112 @default.
- W4377700912 hasConceptScore W4377700912C153180895 @default.
- W4377700912 hasConceptScore W4377700912C153349607 @default.
- W4377700912 hasConceptScore W4377700912C154945302 @default.
- W4377700912 hasConceptScore W4377700912C177264268 @default.
- W4377700912 hasConceptScore W4377700912C194051981 @default.
- W4377700912 hasConceptScore W4377700912C199360897 @default.
- W4377700912 hasConceptScore W4377700912C2777904410 @default.
- W4377700912 hasConceptScore W4377700912C2778137410 @default.
- W4377700912 hasConceptScore W4377700912C2944601119 @default.
- W4377700912 hasConceptScore W4377700912C41008148 @default.
- W4377700912 hasConceptScore W4377700912C41895202 @default.
- W4377700912 hasConceptScore W4377700912C45347329 @default.
- W4377700912 hasConceptScore W4377700912C50644808 @default.
- W4377700912 hasConceptScore W4377700912C75294576 @default.
- W4377700912 hasLocation W43777009121 @default.
- W4377700912 hasOpenAccess W4377700912 @default.
- W4377700912 hasPrimaryLocation W43777009121 @default.
- W4377700912 hasRelatedWork W133358225 @default.
- W4377700912 hasRelatedWork W1728708896 @default.
- W4377700912 hasRelatedWork W2508908072 @default.
- W4377700912 hasRelatedWork W2509146328 @default.
- W4377700912 hasRelatedWork W2810384904 @default.
- W4377700912 hasRelatedWork W2911155898 @default.
- W4377700912 hasRelatedWork W2929240682 @default.
- W4377700912 hasRelatedWork W3177090203 @default.
- W4377700912 hasRelatedWork W3177163058 @default.
- W4377700912 hasRelatedWork W1629725936 @default.
- W4377700912 isParatext "false" @default.
- W4377700912 isRetracted "false" @default.
- W4377700912 workType "article" @default.