2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) 2015
DOI: 10.1109/embc.2015.7319030
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Deep neural network and random forest hybrid architecture for learning to detect retinal vessels in fundus images

Abstract: Vision impairment due to pathological damage of the retina can largely be prevented through periodic screening using fundus color imaging. However the challenge with large-scale screening is the inability to exhaustively detect fine blood vessels crucial to disease diagnosis. In this work we present a computational imaging framework using deep and ensemble learning based hybrid architecture for reliable detection of blood vessels in fundus color images. A deep neural network (DNN) is used for unsupervised lear… Show more

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Cited by 82 publications
(84 citation statements)
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“…For an exhaustive detection of fine retinal vessels, Maji et al [84] designed a hybrid framework of deep and ensemble learning, where a Deep Neural Network (DNN) was used for unsupervised learning of vesselness via denoising auto-encoder, utilizing sparse trained retinal vascular patches. The learned representation of retinal vasculature patches was used as weights in the deep neural network, and the response of deep neural network was used in supervised learning process with a random forest for sake of vasculature tissues identification.…”
Section: Machine Learning Techniquesmentioning
confidence: 99%
“…For an exhaustive detection of fine retinal vessels, Maji et al [84] designed a hybrid framework of deep and ensemble learning, where a Deep Neural Network (DNN) was used for unsupervised learning of vesselness via denoising auto-encoder, utilizing sparse trained retinal vascular patches. The learned representation of retinal vasculature patches was used as weights in the deep neural network, and the response of deep neural network was used in supervised learning process with a random forest for sake of vasculature tissues identification.…”
Section: Machine Learning Techniquesmentioning
confidence: 99%
“…In Table 1, Maji et al [14] have developed a collective learning method using 12 deep CNN models for vessel segmentation, Fu et al [25] have proposed an approach combining CNN and CRF (Conditional Random Field) layers, and Niemeijer et al [26] presented a vessel segmentation algorithm based on pixel classification using a simple feature vector. The proposed method achieved the highest AUC value for the DRIVE dataset.…”
Section: Discussionmentioning
confidence: 99%
“…In future, we will employ some post-processing methods for improving the quality of the vessel detection. Method AUC Maji et al [14] 0.9283 Fu et al [25] 0.9470 Niemeijer et al [26] In Table 2, Kande et al [10] have recommended an unsupervised fuzzy based vessel segmentation method, Jiang et al [2] have proposed an adaptive local thresholding method and Hoover et al [27] also have combined local and region-based properties to segment blood vessels in retinal images. The highest AUC value was also obtained for STARE dataset with the proposed method.…”
Section: Discussionmentioning
confidence: 99%
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