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BibTex Citation Data :
@article{JOIV857, author = {Agus Eko Minarno and Mochammad Hazmi Cokro Mandiri and Yufis Azhar and Fitri Bimantoro and Hanung Adi Nugroho and Zaidah Ibrahim}, title = {Classification of Diabetic Retinopathy Disease Using Convolutional Neural Network}, journal = {JOIV : International Journal on Informatics Visualization}, volume = {6}, number = {1}, year = {2022}, keywords = {Image; classification; diabetic retinopathy; CNN; APTOS.}, abstract = {Diabetic Retinopathy (DR) is a disease that causes visual impairment and blindness in patients with it. Diabetic Retinopathy disease appears characterized by a condition of swelling and leakage in the blood vessels located at the back of the retina of the eye. Early detection through the retinal fundus image of the eye could take time and requires an experienced ophthalmologist. This study proposed a deep learning method, the Efficientnet-b7 model to identify diabetic retinopathy disease automatically. This study applies three preprocessing techniques that could be implemented in the dataset \"APTOS 2019 Blindness Detection\". In preprocessing technique trial scenarios, Usuyama preprocessing technique obtained the best results with accuracy of 89% of train data and 84% in test data compared to Harikrishnan preprocessing technique which has 82% accuracy in test data, and Ben Graham preprocessing has 81% accuracy in test data. In this study, Hyperparameter tuning was conducted to find the best parameters for use on the EfficientNet-B7 Model. In this study, we tested the Efficientnet-B7 model with an augmentation process that can reduce the occurrence of overfitting compared to models without augmentation. Preprocessing techniques and augmentation techniques can influence the proposed EfficientNet-B7 model in terms of performance results and reduce the overfitting of models. }, issn = {2549-9904}, pages = {12--18}, doi = {10.30630/joiv.6.1.857}, url = {http://joiv.org/index.php/joiv/article/view/857} }
Refworks Citation Data :
@article{{JOIV}{857}, author = {Minarno, A., Cokro Mandiri, M., Azhar, Y., Bimantoro, F., Nugroho, H., Ibrahim, Z.}, title = {Classification of Diabetic Retinopathy Disease Using Convolutional Neural Network}, journal = {JOIV : International Journal on Informatics Visualization}, volume = {6}, number = {1}, year = {2022}, doi = {10.30630/joiv.6.1.857}, url = {} }Refbacks
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JOIV : International Journal on Informatics Visualization
ISSN 2549-9610 (print) | 2549-9904 (online)
Organized by Department of Information Technology - Politeknik Negeri Padang, and Institute of Visual Informatics - UKM and Soft Computing and Data Mining Centre - UTHM
W : http://joiv.org
E : joiv@pnp.ac.id, hidra@pnp.ac.id, rahmat@pnp.ac.id
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is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.