TY - JOUR
T1 - Hybrid CNN-SVD Based Prominent Feature Extraction and Selection for Grading Diabetic Retinopathy Using Extreme Learning Machine Algorithm
AU - Nahiduzzaman, Md
AU - Islam, Md Robiul
AU - Islam, S. M.Riazul
AU - Goni, Md Omaer Faruq
AU - Anower, Md Shamim
AU - Kwak, Kyung Sup
N1 - Funding Information:
This work was supported in part by the National Research Foundation of Korea-Grant funded by the Government of Korea (Ministry of Science and ICT) under Grant NRF-2020R1A2B5B02002478, and in part by Sejong University through the Faculty Research Program under Grant 20212023
Publisher Copyright:
© 2013 IEEE.
PY - 2021/11/18
Y1 - 2021/11/18
N2 - This paper exploits the extreme learning machine (ELM) approach to address diabetic retinopathy (DR), a medical condition in which impairment occurs to the retina caused by diabetes. DR, a leading cause of blindness worldwide, is a sort of swelling leakage due to excessive blood sugar in the retina vessels. An early-stage diagnosis is therefore beneficial to prevent diabetes patients from losing their sight. This study introduced a novel method to detect DR for binary class and multiclass classification based on the APTOS-2019 blindness detection and Messidor-2 datasets. First, DR images have been pre-processed using Ben Graham's approach. After that, contrast limited adaptive histogram equalization (CLAHE) has been used to get contrast-enhanced images with lower noise and more distinguishing features. Then a novel hybrid convolutional neural network-singular value decomposition model has been developed to reduce input features for classifiers. Finally, the proposed method uses an ELM algorithm as the classifier that minimizes the training time cost. The experiments focus on accuracy, precision, recall, and F1-score and demonstrate the feasibility of adopting the proposed scheme for DR diagnosis. The method outperforms the existing techniques and shows an optimistic accuracy and recall of 99.73% and 100%, respectively, for binary class. For five stages of DR classification, the proposed model achieved an accuracy of 98.09% and 96.26% for APTOS-2019 and Messidor-2 datasets, respectively, which outperformed the existing state-of-art models.
AB - This paper exploits the extreme learning machine (ELM) approach to address diabetic retinopathy (DR), a medical condition in which impairment occurs to the retina caused by diabetes. DR, a leading cause of blindness worldwide, is a sort of swelling leakage due to excessive blood sugar in the retina vessels. An early-stage diagnosis is therefore beneficial to prevent diabetes patients from losing their sight. This study introduced a novel method to detect DR for binary class and multiclass classification based on the APTOS-2019 blindness detection and Messidor-2 datasets. First, DR images have been pre-processed using Ben Graham's approach. After that, contrast limited adaptive histogram equalization (CLAHE) has been used to get contrast-enhanced images with lower noise and more distinguishing features. Then a novel hybrid convolutional neural network-singular value decomposition model has been developed to reduce input features for classifiers. Finally, the proposed method uses an ELM algorithm as the classifier that minimizes the training time cost. The experiments focus on accuracy, precision, recall, and F1-score and demonstrate the feasibility of adopting the proposed scheme for DR diagnosis. The method outperforms the existing techniques and shows an optimistic accuracy and recall of 99.73% and 100%, respectively, for binary class. For five stages of DR classification, the proposed model achieved an accuracy of 98.09% and 96.26% for APTOS-2019 and Messidor-2 datasets, respectively, which outperformed the existing state-of-art models.
KW - Ben Graham's pre-processing
KW - contrast limited adaptive histogram equalization (CLAHE)
KW - convolutional neural network-singular value decomposition (CNN-SVD)
KW - diabetic retinopathy (DR)
KW - extreme learning machine (ELM)
UR - http://www.scopus.com/inward/record.url?scp=85120352105&partnerID=8YFLogxK
U2 - 10.1109/ACCESS.2021.3125791
DO - 10.1109/ACCESS.2021.3125791
M3 - Article
AN - SCOPUS:85120352105
VL - 9
SP - 152261
EP - 152274
JO - IEEE Access
JF - IEEE Access
SN - 2169-3536
ER -