4.6 Article

Diabetic retinopathy detection using convolutional neural network with residual blocks

期刊

出版社

ELSEVIER SCI LTD
DOI: 10.1016/j.bspc.2023.105494

关键词

Deep learning; Diabetic retinopathy; Convolutional neural network; Residual network

向作者/读者索取更多资源

Diabetic Retinopathy (DR) is a serious retinal disease that requires early detection to prevent blindness. Researchers proposed a deep learning model called DRCNNRB to detect blood abnormalities in retinal images. This model utilizes data augmentation and image preprocessing techniques to address the problem of data imbalance and achieve better performance compared to other approaches.
Diabetic Retinopathy (DR) is a disease that happens in the patient eyes of long-term diabetics. It also affects the retina which causes eye blindness. Therefore, DR has to be detected at its early stage to decrease the risk of blindness. Several researchers suggested approaches to detect the blood abnormalities (hemorrhages, Hard and soft exudates, and micro-aneurysms) in the retina images using deep learning models. The limitation with these approaches is the performance degradation and required high training time. To solve this, we suggest a model for automated detection of DR severity using a convolutional neural network (CNN) and residual blocks (DRCNNRB). Deep learning models work effectively when they have been trained on vast datasets. Data Augmentation helps to increase the training samples as a result avoids the data imbalance problem. In our model, basic data augmentation techniques such as zooming, shearing, rotation, flipping, and rescaling are applied in DRCNNRB to solve the data imbalance problem. Pre-processing techniques are used to enhance the quality of the image. Extensive experimental results on the Diabetic Retinopathy 2015 Data Colored Resized database conclude that DRCNNRB provides better performance compared to other state-of-the-art works. Thus, DRCNNRB achieves better efficiency for real-time diagnosis.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.6
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据