Deep Learning-Based Diabetic Retinopathy Severity Grading System Employing Quadrant Ensemble Model
Deep Learning-Based Diabetic Retinopathy Severity Grading System Employing Quadrant Ensemble Model
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DOI：
10.1007/s10278-021-00418-5
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发表时间：
2021-03-08
影响因子：
4.4
通讯作者：
Sood, Meenakshi
中科院分区：
文献类型：
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作者：
Bhardwaj, Charu;Jain, Shruti;Sood, Meenakshi
糖尿病视网膜病变在视网膜血管恶化导致严重的汇编影响眼睛。自动DR诊断框架对于早期识别和发现这些眼部相关问题至关重要，有助于眼科专家为有效治疗提供第二意见。深度学习技术是对传统方法的改进，传统方法依赖于手工特征提取。为了解决熟练的DR判别问题，作者通过实现InceptionResnet-V2深度神经网络框架，提出了一种象限集成自动DR分级方法。该模型结合了直方图均衡化、光盘定位和象限裁剪以及数据增强步骤来提高网络性能。该框架的准确率达到了93.33%，MESSIDOR基准数据集的交叉熵损失函数显著降低了0.325；然而，利用最新IDRiD数据集的验证建立了其泛化能力。与经典的Inception-V3 CNN模型相比，提出的QEIRV-2模型的准确率提高了13.58%。为了证明所提出的框架的可行性，将其性能与现有最先进的方法进行了比较，观察到其精度提高了25.23%。
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.