@ARTICLE{Asvial_Muhamad_Classification_2024,
 author={Asvial, Muhamad and Silalahi, Tobias Ivandito Margogo and Laagu, Muh. Asnoer},
 volume={vol. 70},
 number={No 4},
 journal={International Journal of Electronics and Telecommunications},
 pages={871-877},
 howpublished={online},
 year={2024},
 publisher={Polish Academy of Sciences Committee of Electronics and Telecommunications},
 abstract={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.},
 type={Article},
 title={Classification system from Optical Coherence Tomography using transfer learning},
 URL={http://czasopisma.pan.pl/Content/133212/PDF-MASTER/12-4722-Asvial-sk.pdf},
 doi={10.24425/ijet.2024.152072},
 keywords={Deep Learning, Transfer Learning, Optical Coherence Tomography, Inception V3, VGG-16},
}