| dc.description.abstract | Diabetic retinopathy is a serious complication of diabetes mellitus that can lead
to blindness if not detected and treated early. This study aims to develop a
Convolutional Neural Network (CNN)-based machine learning model with
EfficientNetB3 architecture to categorize and determine the severity of diabetic
retinopathy through analysis of retinal OCT fundus images. The research methodology
includes downloading a dataset from Kaggle consisting of 10,000 retinal images
divided into five classes: Healthy, Mild Diabetic Retinopathy, Moderate Diabetic
Retinopathy, Severe Diabetic Retinopathy, and Proliferative Diabetic Retinopathy,
each class containing 2000 images. The data preprocessing process was carried out by
resizing the image from 256x256 pixels to 224x224 pixels and applying data
augmentation in the form of brightness, rotation and flip to increase the variation of
the dataset. The EfficientNetB3 model was implemented using transfer learning with
the TensorFlow/Keras framework, with batch normalization, dense, and dropout layers
added, and then compiled using the Adam optimizer, binary crossentropy loss function,
and accuracy metrics. The dataset was divided into 90% training data and 10%
validation data, with the application of data augmentation techniques, weighted loss,
cosine annealing, and test time augmentation (TTA) to address class imbalance and
improve model generalization. The results showed that the model successfully
achieved a validation accuracy of 95.8% with an average F1-score of 0.9570. The best
performance was shown in the classification of healthy eyes (Healthy) with an F1-
score of 0.98, while the Mild and Moderate classes showed slightly lower performance
due to similar visual characteristics. Evaluation through confusion matrix and
classification report confirmed that the model has high precision and recall in almost
all classes. | en_US |
