Deep Learning and Its Application in Diabetic Retinopathy Screening

Zou, Beiji; Xi, Shan; Zhu, Chengzhang; Dai, Yulan; Yue, Kejuan; Chen, Yuanqiong; Xiao, Yalong; Huang, Jiaer

doi:10.1049/cje.2020.09.001

Cited by 4 publications

(1 citation statement)

References 59 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Deep convolutional neural networks [15] (DCNN) have achieved excellent results in a variety of computing tasks [1], [12], [13], [16], but training them requires a considerable number of fully annotated datasets. Significantly, the task of semantic segmentation [1], [2] demands fully supervised pixel-level labels, which are costly to obtain.…”

Section: Related Workmentioning

confidence: 99%

Attention Guided Enhancement Network for Weakly Supervised Semantic Segmentation

Zhe

Wang

et al. 2023

Chinese J. Elect.

View full text Add to dashboard Cite

Weakly supervised semantic segmentation using only image-level labels is critical since it alleviates the need for expensive pixel-level labels. Most cuttingedge methods adopt two-step solutions that learn to produce pseudo-ground-truth using only image-level labels and then train off-the-shelf fully supervised semantic segmentation network with these pseudo labels. Although these methods have made significant progress, they also increase the complexity of the model and training. In this paper, we propose a one-step approach for weakly supervised image semantic segmentation -attention guided enhancement network (AGEN), which produces pseudopixel-level labels under the supervision of image-level labels and trains the network to generate segmentation masks in an end-to-end manner. Particularly, we employ class activation maps (CAM) produced by different layers of the classification branch to guide the segmentation branch to learn spatial and semantic information. However, the CAM produced by the lower layer can capture the complete object region but with many noises. Thus, the self-attention module is proposed to enhance object regions adaptively and suppress irrelevant object regions, further boosting the segmentation performance. Experiments on the Pascal VOC 2012 dataset demonstrate that AGEN outperforms alternative state-of-the-art weakly supervised semantic segmentation methods exclusively relying on image-level labels.

show abstract

Section: Related Workmentioning

confidence: 99%

Attention Guided Enhancement Network for Weakly Supervised Semantic Segmentation

Zhe

Wang

et al. 2023

Chinese J. Elect.

View full text Add to dashboard Cite

show abstract

Deep learning in the grading of diabetic retinopathy: A review

Tajudin

Kipli

Mahmood

et al. 2022

IET Computer Vision

View full text Add to dashboard Cite

Diabetic Retinopathy (DR) grading into different stages of severity continues to remain a challenging issue due to the complexities of the disease. Diabetic Retinopathy grading classifies retinal images to five levels of severity ranging from 0 to 5, which represents No DR, Mild non-proliferative diabetic retinopathy (NPDR), Moderate NPDR, Severe NPDR, and proliferative diabetic retinopathy. With the advancement of Deep Learning, studies on the application of the Convolutional Neural Network (CNN) in DR grading have been on the rise. High accuracy and sensitivity are the desired outcome of these studies. This paper reviewed recently published studies that employed CNN for DR grading to 5 levels of severity. Various approaches are applied in classifying retinal images which are, (i) by training CNN models to learn the features for each grade and (ii) by detecting and segmenting lesions using information about their location such as microaneurysms, exudates, and haemorrhages. Public and private datasets have been utilised by researchers in classifying retinal images for DR. The performance of the CNN models was measured by accuracy, specificity, sensitivity, and area under the curve. The CNN models and their performance varies for every study. More research into the CNN model is necessary for future work to improve model performance in DR grading. The Inception model can be used as a starting point for subsequent research. It will also be necessary to investigate the attributes that the model uses for grading.

show abstract

Network Attack Traffic Recognition Based on Quantum Neural Network

Zhang

Liu

2022

2022 7th International Conference on Computational Intelligence and Applications (ICCIA)

View full text Add to dashboard Cite

Deep Learning and Its Application in Diabetic Retinopathy Screening

Cited by 4 publications

References 59 publications

Attention Guided Enhancement Network for Weakly Supervised Semantic Segmentation

Attention Guided Enhancement Network for Weakly Supervised Semantic Segmentation

Deep learning in the grading of diabetic retinopathy: A review

Network Attack Traffic Recognition Based on Quantum Neural Network

Contact Info

Product

Resources

About