TY - GEN
T1 - Hybrid Method to Identify Diabetic Retinopathy
AU - Novitasari, Dian Candra Rini
AU - Fatmawati,
AU - Hendradi, Rimuljo
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Convolutional Neural Network (CNN) is a deep learning method that performs well in the image data processing. The disadvantage of CNN is that it takes a long time for training and requires a lot of computer memory, so in this study, it is proposed to use the Hybrid method (Convolutional feature learning and Extreme Learning Machine classification) to overcome these problems. The Hybrid method Convolution Extreme Learning Machine (CELM) will classify fundus images of Diabetic Retinopathy (DR). World Health Organization (WHO) recognizes that DR is a significant eye disease that causes blindness and requires special attention because this disease is increasing quickly. The processes carried out in this research are preprocessing (Cropping, Resize, and Augmentation) and classification using CELM. The feature learning process extracts features of the image using various CNN architecture and classified by KELM. The overall accuracy result is obtained by the CELM method, which reaches 99.95% of accuracy and the best architecture obtained on ResNet50 using 800 hidden nodes and it produces a short training time of 1,539 seconds.
AB - Convolutional Neural Network (CNN) is a deep learning method that performs well in the image data processing. The disadvantage of CNN is that it takes a long time for training and requires a lot of computer memory, so in this study, it is proposed to use the Hybrid method (Convolutional feature learning and Extreme Learning Machine classification) to overcome these problems. The Hybrid method Convolution Extreme Learning Machine (CELM) will classify fundus images of Diabetic Retinopathy (DR). World Health Organization (WHO) recognizes that DR is a significant eye disease that causes blindness and requires special attention because this disease is increasing quickly. The processes carried out in this research are preprocessing (Cropping, Resize, and Augmentation) and classification using CELM. The feature learning process extracts features of the image using various CNN architecture and classified by KELM. The overall accuracy result is obtained by the CELM method, which reaches 99.95% of accuracy and the best architecture obtained on ResNet50 using 800 hidden nodes and it produces a short training time of 1,539 seconds.
KW - CELM
KW - CNN
KW - Classification
KW - Diabetic Retinopathy
KW - Feature Learning
UR - http://www.scopus.com/inward/record.url?scp=85144591063&partnerID=8YFLogxK
U2 - 10.1109/IConEEI55709.2022.9972313
DO - 10.1109/IConEEI55709.2022.9972313
M3 - Conference contribution
AN - SCOPUS:85144591063
T3 - Proceedings of the International Conference on Electrical Engineering and Informatics
SP - 64
EP - 69
BT - ICon EEI 2022 - 3rd International Conference on Electrical Engineering and Informatics
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 3rd International Conference on Electrical Engineering and Informatics, ICon EEI 2022
Y2 - 19 October 2022 through 20 October 2022
ER -
