TY - GEN
T1 - Classification of Diabetic Retinopathy through Deep Feature Extraction and Classic Machine Learning Approach
AU - Paradisa, Radifa Hilya
AU - Sarwinda, Devvi
AU - Bustamam, Alhadi
AU - Argyadiva, Terry
N1 - Funding Information:
ACKNOWLEDGMENT This research was supported by the PUTI Saintekes research grant with contract number NKB-2401/UN2.RST/HKP.05.00/2020. The authors appreciate colleagues from the Directorate of Research and Community Engagement Universitas Indonesia who contributed insights and expertise to advance this research in innumerable ways.
Publisher Copyright:
© 2020 IEEE.
Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.
PY - 2020/11/24
Y1 - 2020/11/24
N2 - Diabetic Retinopathy (DR) is a complication of diabetes, the leading cause of vision loss in working-age adults. An ophthalmologist can carry out the diagnosis of DR by examining color fundus images. However, the fundus image analysis process takes a long time. Automatic detection of DR is achallenging task. One of the deep learning approaches, Convolutional Neural Networks (CNN), is efficient in image classification tasks. In this research, a CNN architecture is used, namely ResNet-50, as feature extraction and classification. The ResNet-50 feature output at the feature extraction stage is also used as input for machine learning classifiers such as Support Vector Machine (SVM), Random Forest (RF), k-Nearest Neighbor (k-NN), and Extreme Gradient Boosting (XGBoost). The model works by using fundus images from the DIARETDBI dataset. Data augmentation and preprocessing are proposed in this study to facilitate the model in recognizing images. The performance of each classifier is evaluated based on accuracy, sensitivity, and specificity. The SVM classifier achieved 99% for accuracy and sensitivity in the 80:20 dataset composition. The k-NN classifier obtains the highest specificity for the same dataset's design by 100%.
AB - Diabetic Retinopathy (DR) is a complication of diabetes, the leading cause of vision loss in working-age adults. An ophthalmologist can carry out the diagnosis of DR by examining color fundus images. However, the fundus image analysis process takes a long time. Automatic detection of DR is achallenging task. One of the deep learning approaches, Convolutional Neural Networks (CNN), is efficient in image classification tasks. In this research, a CNN architecture is used, namely ResNet-50, as feature extraction and classification. The ResNet-50 feature output at the feature extraction stage is also used as input for machine learning classifiers such as Support Vector Machine (SVM), Random Forest (RF), k-Nearest Neighbor (k-NN), and Extreme Gradient Boosting (XGBoost). The model works by using fundus images from the DIARETDBI dataset. Data augmentation and preprocessing are proposed in this study to facilitate the model in recognizing images. The performance of each classifier is evaluated based on accuracy, sensitivity, and specificity. The SVM classifier achieved 99% for accuracy and sensitivity in the 80:20 dataset composition. The k-NN classifier obtains the highest specificity for the same dataset's design by 100%.
KW - Convolutional Neural Network
KW - Diabetic Retinopathy
KW - Feature Extraction
KW - Machine Learning Classifier
KW - ResNet-50
UR - http://www.scopus.com/inward/record.url?scp=85100907188&partnerID=8YFLogxK
U2 - 10.1109/ICOIACT50329.2020.9332082
DO - 10.1109/ICOIACT50329.2020.9332082
M3 - Conference contribution
AN - SCOPUS:85100907188
T3 - 2020 3rd International Conference on Information and Communications Technology, ICOIACT 2020
SP - 377
EP - 381
BT - 2020 3rd International Conference on Information and Communications Technology, ICOIACT 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 3rd International Conference on Information and Communications Technology, ICOIACT 2020
Y2 - 24 November 2020 through 25 November 2020
ER -
