Classification System from Optical Coherence Tomography Using Transfer Learning

• Autorzy: Asvial Muhamad , Silalahi Tobias Ivandito Margogo , Laagu Muh Asnoer

Identyfikatory

DOI

10.24425/ijet.2024.152072

• Języki publikacji: EN

Abstrakty

EN

This research aims to create a decision support system to identify retinal diseases using a four-class classification problem. To achieve this, the proposed system uses deep learning architecture to automatically recognize CNV, DME, and drusen from OCT images. The model employs two transfer learning architectures with several additional layers to classify retinal diseases. The purpose of model training, validation, and testing, the experiment uses 6,000 grayscale images labeled into four classes from the OCT data set. The Inception V3 model's proposed additional layer exhibits an increase in accuracy of 3.08% and a reduction in the loss by 0.3767. The experiment's results indicate that the Inception V3 model achieved an accuracy rate of 99.31%, and the VGG-16 model reached 98.83%, which outperformed other deep learning models using the OCT data set.

Słowa kluczowe

EN

optical coherence tomography; OCT; Inception V3; VGG-16; deep learning; transfer learning

• Wydawca: Polish Academy of Sciences, Committee of Electronics and Telecommunication
• Czasopismo: International Journal of Electronics and Telecommunications
• Rocznik: 2024
• Tom: Vol. 70, No. 4
• Strony: 871--877
• Opis fizyczny: Bibliogr. 19 poz., tab., rys.

Twórcy

autor

Asvial Muhamad

• Universitas Indonesia

autor

Silalahi Tobias Ivandito Margogo

• Universitas Indonesia

autor

Laagu Muh Asnoer

• University of Jember

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki (2025).