Discriminative kernel convolution network for multi-label ophthalmic disease detection on imbalanced fundus image dataset

Amit Bhati; Neha Gour; Pritee Khanna; Aparajita Ojha

doi:10.1016/j.compbiomed.2022.106519

journal article Feb 01, 2023

Discriminative kernel convolution network for multi-label ophthalmic disease detection on imbalanced fundus image dataset

Amit Bhati Neha Gour Pritee Khanna

Aparajita Ojha

Computers in Biology and Medicine Vol. 153 pp. 106519 · Elsevier BV

View at Publisher Save 10.1016/j.compbiomed.2022.106519

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References

39

[1]

Burton "The lancet global health commission on global eye health" (2021)

[2]

Kawasaki "Clinical motivation and the needs for RIA in healthcare" (2019)

[3]

Abramoff "Retinal imaging and image analysis" IEEE Rev. Biomed. Eng. (2010) 10.1109/rbme.2010.2084567

[4]

Sekou (2019)

[5]

Fu "Joint optic disc and cup segmentation based on multi-label deep network and polar transformation" IEEE Trans. Med. Imaging (2018) 10.1109/tmi.2018.2791488

[6]

Li "Applications of deep learning in fundus images: A review" Med. Image Anal. (2021) 10.1016/j.media.2021.101971

[7]

Deep convolution neural network for accurate diagnosis of glaucoma using digital fundus images

U Raghavendra, Hamido Fujita, Sulatha V Bhandary et al.

Information Sciences 2018 10.1016/j.ins.2018.01.051

[8]

Disc-Aware Ensemble Network for Glaucoma Screening From Fundus Image

Huazhu Fu, Jun Cheng, Yanwu Xu et al.

IEEE Transactions on Medical Imaging 2018 10.1109/tmi.2018.2837012

[9]

Gour "Challenges for ocular disease identification in the era of artificial intelligence" Neural Comput. Appl. (2022)

[10]

Odir, Peking University International Competition on Ocular Disease Intelligent Recognition, ODIR-2019, 2019.

[11]

Lin "The SUStech-SYSU dataset for automated exudate detection and diabetic retinopathy grading" Sci. Data (2020) 10.1038/s41597-020-00755-0

[12]

Jiang "Optic disc and cup segmentation with blood vessel removal from fundus images for glaucoma detection" (2018)

[13]

Giancardo "Exudate-based diabetic macular edema detection in fundus images using publicly available datasets" Med. Image Anal. (2012) 10.1016/j.media.2011.07.004

[14]

Luo "Dehaze of cataractous retinal images using an unpaired generative adversarial network" IEEE J. Biomed. Health Inf. (2020) 10.1109/jbhi.2020.2999077

[15]

Antonio "Factors associated with changes in retinal microcirculation after antihypertensive treatment" J. Hum. Hypertens. (2014) 10.1038/jhh.2013.108

[16]

Wang "Artificial intelligence and deep learning in ophthalmology" Artif. Intell. Med. (2020)

[17]

Islam "Source and camera independent ophthalmic disease recognition from fundus image using neural network" (2019)

[18]

Wang "Multi-label classification of fundus images with efficientnet" IEEE Access (2020) 10.1109/access.2020.3040275

[19]

Li "Dense correlation network for automated multi-label ocular disease detection with paired color fundus photographs" (2020)

[20]

Gour "Multi-class multi-label ophthalmological disease detection using transfer learning based convolutional neural network" Biomed. Signal Process. Control (2021) 10.1016/j.bspc.2020.102329

[21]

Simonyan (2014)

[22]

Li "A benchmark of ocular disease intelligent recognition: one shot for multi-disease detection" (2020)

[23]

Densely Connected Convolutional Networks

Gao Huang, Zhuang Liu, Laurens Van Der Maaten et al.

2017 IEEE Conference on Computer Vision and Patter... 10.1109/cvpr.2017.243

[24]

Lin "Multi-label classification of fundus images with graph convolutional network and self-supervised learning" IEEE Signal Process. Lett. (2021) 10.1109/lsp.2021.3057548

[25]

Ou "Bfenet: A two-stream interaction CNN method for multi-label ophthalmic diseases classification with bilateral fundus images" Comput. Methods Programs Biomed. (2022) 10.1016/j.cmpb.2022.106739

[26]

A systematic study of the class imbalance problem in convolutional neural networks

Mateusz Buda, Atsuto Maki, Maciej A. Mazurowski

Neural Networks 2018 10.1016/j.neunet.2018.07.011

[27]

Convolutional Neural Networks for Diabetic Retinopathy

Harry Pratt, Frans Coenen, Deborah M. Broadbent et al.

Procedia Computer Science 2016 10.1016/j.procs.2016.07.014

[28]

Muthukannan "Optimized convolution neural network based multiple eye disease detection" Comput. Biol. Med. (2022)

[29]

Yu (2015)

[30]

Zhang "A novel ResNet101 model based on dense dilated convolution for image classification" SN Appl. Sci. (2022) 10.1007/s42452-021-04897-7

[31]

Ku "Multilevel feature fusion dilated convolutional network for semantic segmentation" Int. J. Adv. Robot. Syst. (2021) 10.1177/17298814211007665

[32]

Sun "Multi-label classification of fundus images with graph convolutional network and lightgbm" Comput. Biol. Med. (2022) 10.1016/j.compbiomed.2022.105909

[33]

Going deeper with convolutions

Christian Szegedy, Wei Liu, Yangqing Jia et al.

2015 IEEE Conference on Computer Vision and Patter... 10.1109/cvpr.2015.7298594

[34]

Deep Residual Learning for Image Recognition

Kaiming He, Xiangyu Zhang, Shaoqing Ren et al.

2016 IEEE Conference on Computer Vision and Patter... 10.1109/cvpr.2016.90

[35]

Howard (2017)

[36]

Pyramid Scene Parsing Network

Hengshuang Zhao, Jianping Shi, Xiaojuan Qi et al.

2017 IEEE Conference on Computer Vision and Patter... 10.1109/cvpr.2017.660

[37]

Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation

Liang-Chieh Chen, Yukun Zhu, George Papandreou et al.

Lecture Notes in Computer Science 10.1007/978-3-030-01234-2_49

[38]

Squeeze-and-Excitation Networks

Jie Hu, Li Shen, Gang Sun

2018 IEEE/CVF Conference on Computer Vision and Pa... 10.1109/cvpr.2018.00745

[39]

Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization

Ramprasaath R. Selvaraju, Michael Cogswell, Abhishek Das et al.

2017 IEEE International Conference on Computer Vis... 10.1109/iccv.2017.74

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Details

Published: Feb 01, 2023
Vol/Issue: 153
Pages: 106519
License: View

Authors

People’s Dental Academy, Bhopal, Madhya Pradesh , India

Cite This Article

Amit Bhati, Neha Gour, Pritee Khanna, et al. (2023). Discriminative kernel convolution network for multi-label ophthalmic disease detection on imbalanced fundus image dataset. Computers in Biology and Medicine, 153, 106519. https://doi.org/10.1016/j.compbiomed.2022.106519

Discriminative kernel convolution network for multi-label ophthalmic disease detection on imbalanced fundus image dataset

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