TY - JOUR
T1 - RAG-FW
T2 - A Hybrid Convolutional Framework for the Automated Extraction of Retinal Lesions and Lesion-Influenced Grading of Human Retinal Pathology
AU - Hassan, Taimur
AU - Akram, Muhammad Usman
AU - Werghi, Naoufel
AU - Nazir, Muhammad Noman
N1 - Funding Information:
Manuscript received November 8, 2019; revised February 15, 2020 and March 11, 2020; accepted March 18, 2020. Date of publication March 27, 2020; date of current version January 5, 2021. This work was supported by a research fund from Khalifa University: Ref: CIRA-2019-047. (Corresponding author: Taimur Hassan.) Taimur Hassan is with the Center for Cyber-Physical Systems, EECS Department, Khalifa University, Abu Dhabi 127788, UAE, and also with the Department of Computer and Software Engineering, National University of Sciences and Technology, Islamabad 24090, Pakistan (e-mail: [email protected]).
Publisher Copyright:
© 2013 IEEE.
PY - 2021/1
Y1 - 2021/1
N2 - The identification of retinal lesions plays a vital role in accurately classifying and grading retinopathy. Many researchers have presented studies on optical coherence tomography (OCT) based retinal image analysis over the past. However, to the best of our knowledge, there is no framework yet available that can extract retinal lesions from multi-vendor OCT scans and utilize them for the intuitive severity grading of the human retina. To cater this lack, we propose a deep retinal analysis and grading framework (RAG-FW). RAG-FW is a hybrid convolutional framework that extracts multiple retinal lesions from OCT scans and utilizes them for lesion-influenced grading of retinopathy as per the clinical standards. RAG-FW has been rigorously tested on 43,613 scans from five highly complex publicly available datasets, containing multi-vendor scans, where it achieved the mean intersection-over-union score of 0.8055 for extracting the retinal lesions and the accuracy of 98.70% for the correct severity grading of retinopathy.
AB - The identification of retinal lesions plays a vital role in accurately classifying and grading retinopathy. Many researchers have presented studies on optical coherence tomography (OCT) based retinal image analysis over the past. However, to the best of our knowledge, there is no framework yet available that can extract retinal lesions from multi-vendor OCT scans and utilize them for the intuitive severity grading of the human retina. To cater this lack, we propose a deep retinal analysis and grading framework (RAG-FW). RAG-FW is a hybrid convolutional framework that extracts multiple retinal lesions from OCT scans and utilizes them for lesion-influenced grading of retinopathy as per the clinical standards. RAG-FW has been rigorously tested on 43,613 scans from five highly complex publicly available datasets, containing multi-vendor scans, where it achieved the mean intersection-over-union score of 0.8055 for extracting the retinal lesions and the accuracy of 98.70% for the correct severity grading of retinopathy.
KW - deep learning
KW - Ophthalmo-logy
KW - optical coherence tomography (OCT)
KW - Retinopathy
UR - http://www.scopus.com/inward/record.url?scp=85083744398&partnerID=8YFLogxK
U2 - 10.1109/JBHI.2020.2982914
DO - 10.1109/JBHI.2020.2982914
M3 - Article
C2 - 32224467
AN - SCOPUS:85083744398
SN - 2168-2194
VL - 25
SP - 108
EP - 120
JO - IEEE Journal of Biomedical and Health Informatics
JF - IEEE Journal of Biomedical and Health Informatics
IS - 1
M1 - 9049083
ER -