Glaucoma detection based on deep convolutional neural network

By Xiangyu Chen, Yanwu Xu, Damon Wing Kee Wong, Tien Yin Wong, Jiang Liu. Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2015

383 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Ophthalmology, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/d95abb4a05e21e4e2929c3cfccf58abce7d18796

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Yuan Xue
2. Nianyin Zeng
3. Wei Wei
4. Weibo Liu
5. Xiaohui Liu
6. Zidong Wang
7. Fuad E. Alsaadi
8. Yurong Liu

In Radiology, Nuclear Medicine and Imaging

1. Olivier Elemento
2. Li-Jia Li
3. Dragica Radosav
4. M. Bakator

Citations in the corpus

• Deep Learning and Medical Diagnosis: A Review of Literature (2018, positive): “[20] CNN Fundus images Glaucoma detection; the experiments were performed on SCES and ORIGA datasets; further, it was noted that this approach may be great for glaucoma detection.”
• Thoracic Disease Identification and Localization with Limited Supervision (2017, background): “It is usually formulated as a classification problem where medical imaging abnormalities are identified as different clinical conditions [25, 4, 26, 28, 34].”
• Performance of Deep Learning Architectures and Transfer Learning for Detecting Glaucomatous Optic Neuropathy in Fundus Photographs (2018, neutral): “45 .”
• A survey of deep neural network architectures and their applications (2017, background): “Besides, deep learning techniques have also been applied to Glaucoma detection [23], and human-robot interaction systems with promising results [82].”
• Deep Convolutional Neural Networks Enable Discrimination of Heterogeneous Digital Pathology Images (2017, background): “Currently, the CNNs with deeper architecture and hierarchical feature representation learning have made dramatic changes in object recognition related problems (Russakovsky et al., 2014; Krizhevsky et al., 2012; Szegedy et al., 2015; Simonyan and Zisserman, 2014; Chen et al., 2015).”
• REFUGE Challenge: A Unified Framework for Evaluating Automated Methods for Glaucoma Assessment from Fundus Photographs (2019, neutral): “of the literature in glaucoma classication uses receiver-operating characteristic (ROC) curves (Davis and Goadrich, 2006) for evaluation, including the area under the curve (AUC) as a summary value (Chen et al., 2015a,b; Christopher et al., 2018; Fu et al., 2018; Gomez-Valverde et al., 2019; Orlando”
• CNNs for automatic glaucoma assessment using fundus images: an extensive validation (2019, methodology): “Once the models were fine-tuned, 46 ms are needed to assign a glaucoma probability for each retinal image.”