Classification and segmentation of periodontal cystfor digital dental diagnosis using deep learning

Lakshmi, T. K.; Dheeba, J.

doi:10.24423/cames.505

Artykuł - szczegóły

Tytuł artykułu

Classification and segmentation of periodontal cystfor digital dental diagnosis using deep learning

Autorzy

Lakshmi T. K. , Dheeba J.

Treść / Zawartość

Pełne teksty:

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Identyfikatory

DOI

10.24423/cames.505

Warianty tytułu

Języki publikacji

Abstrakty

The digital revolution is changing every aspect of life by simulating the ways humansthink, learn and make decisions. Dentistry is one of the major fields where subsets ofartificial intelligence are extensively used for disease predictions. Periodontitis, the mostprevalent oral disease, is the main focus of this study. We propose methods for classifyingand segmenting periodontal cysts on dental radiographs using CNN, VGG16, and U-Net.Accuracy of 77.78% is obtained using CNN, and enhanced accuracy of 98.48% is obtainedthrough transfer learning with VGG16. The U-Net model also gives encouraging results.This study presents promising results, and in the future, the work can be extended withother pre-trained models and compared. Researchers working in this field can develop novelmethods and approaches to support dental practitioners and periodontists in decision-making and diagnosis and use artificial intelligence to bridge the gap between humansand machines.

Słowa kluczowe

CNN dental radiograph deep learning health care machine transfer learning periodontal cyst predictive analytics segmentation U-Net VGG16

CNN rentgenowskie zdjęcie zębów uczenie głębokie opieka zdrowotna uczenie się z transferu maszynowego torbiel przyzębia analityka predykcyjna segmentacja U-Net VGG16

Wydawca

Instytut Podstawowych Problemów Techniki PAN

Czasopismo

Computer Assisted Methods in Engineering and Science

Rocznik

2023

Tom

Vol. 30, no. 2

Strony

131--149

Opis fizyczny

Bibliogr. 47 poz., il., wykr.

Twórcy

autor

Lakshmi T. K.

tlakshmiphd@gmail.com

School of Computer Science and Engineering (SCOPE), Vellore Institute of Technology, Vellore, India

autor

Dheeba J.

School of Computer Science and Engineering (SCOPE), Vellore Institute of Technology, Vellore, India

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).

Typ dokumentu

Bibliografia

Identyfikator YADDA

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