TY - GEN
T1 - Identifying the Level of Diabetic Retinopathy Using Deep Convolution Neural Network
AU - Hassan, Rahat
AU - Rahman, Md Arafatur
AU - Ullah, Ihsan
AU - Hamdan Alenezi, Ali
AU - Rassem, Taha H.
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/12/21
Y1 - 2020/12/21
N2 - Diabetic Retinopathy is the leading cause of blindness in the last 100 years. The traditional screening process for DR and its stages takes a lot of time, and it is not practical. Using machine learning techniques and image processing, we can automate detecting diabetic retinal disease and disease stage with acceptable performance. In this work, we have used multiple deep convolution neural networks (CNN) with the same architecture of InceptionV3. Each of the pre-trained Inception V3 architecture is retrained with 2200 preprocessed and leveled images. The dataset is preprocessed using multiple high performing and effective image processing techniques. Then the newly trained models are used for identifying the level of DR. In the final stage, we use a voting scheme for classifying the level of DR from the output of each model. We have achieved 90.5% accuracy in binary classification (Normal/DR) and 81.1% accuracy in 5-class classification.
AB - Diabetic Retinopathy is the leading cause of blindness in the last 100 years. The traditional screening process for DR and its stages takes a lot of time, and it is not practical. Using machine learning techniques and image processing, we can automate detecting diabetic retinal disease and disease stage with acceptable performance. In this work, we have used multiple deep convolution neural networks (CNN) with the same architecture of InceptionV3. Each of the pre-trained Inception V3 architecture is retrained with 2200 preprocessed and leveled images. The dataset is preprocessed using multiple high performing and effective image processing techniques. Then the newly trained models are used for identifying the level of DR. In the final stage, we use a voting scheme for classifying the level of DR from the output of each model. We have achieved 90.5% accuracy in binary classification (Normal/DR) and 81.1% accuracy in 5-class classification.
KW - Contrast Limited Adaptive Histogram Equalization (CLAHE)
KW - Convolution Neural Network (CNN)
KW - Diabetic Retinopathy
KW - InceptionV3
UR - http://www.scopus.com/inward/record.url?scp=85102021984&partnerID=8YFLogxK
U2 - 10.1109/ETCCE51779.2020.9350905
DO - 10.1109/ETCCE51779.2020.9350905
M3 - Conference Publication
AN - SCOPUS:85102021984
T3 - ETCCE 2020 - International Conference on Emerging Technology in Computing, Communication and Electronics
BT - ETCCE 2020 - International Conference on Emerging Technology in Computing, Communication and Electronics
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 International Conference on Emerging Technology in Computing, Communication and Electronics, ETCCE 2020
Y2 - 21 December 2020 through 22 December 2020
ER -