Open AccessDeep Learning–Based Diabetic Retinopathy Severity Grading System Employing Quadrant Ensemble Model
Author(s) -
Charu Bhardwaj,
Shruti Jain,
Meenakshi Sood
Publication year - 2021
Publication title -
journal of digital imaging
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.055
H-Index - 58
eISSN - 1618-727X
pISSN - 0897-1889
DOI - 10.1007/s10278-021-00418-5
Subject(s) - computer science , artificial intelligence , deep learning , diabetic retinopathy , grading (engineering) , pattern recognition (psychology) , machine learning , histogram equalization , histogram , medicine , image (mathematics) , civil engineering , engineering , diabetes mellitus , endocrinology
The diabetic retinopathy accounts in the deterioration of retinal blood vessels leading to a serious compilation affecting the eyes. The automated DR diagnosis frameworks are critically important for the early identification and detection of these eye-related problems, helping the ophthalmic experts in providing the second opinion for effectual treatment. The deep learning techniques have evolved as an improvement over the conventional approaches, which are dependent on the handcrafted feature extraction. To address the issue of proficient DR discrimination, the authors have proposed a quadrant ensemble automated DR grading approach by implementing InceptionResnet-V2 deep neural network framework. The presented model incorporates histogram equalization, optical disc localization, and quadrant cropping along with the data augmentation step for improving the network performance. A superior accuracy performance of 93.33% is observed for the proposed framework, and a significant reduction of 0.325 is noticed in the cross-entropy loss function for MESSIDOR benchmark dataset; however, its validation utilizing the latest IDRiD dataset establishes its generalization ability. The accuracy improvement of 13.58% is observed when the proposed QEIRV-2 model is compared with the classical Inception-V3 CNN model. To justify the viability of the proposed framework, its performance is compared with the existing state-of-the-art approaches and 25.23% of accuracy improvement is observed.