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Research Article
Sathananthavathi V
Mepco Schlenk Engineering College
Corresponding Author
ORCiD: https://orcid.org/0000-0002-8732-8652
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This work is licensed under a CC BY 4.0 License. Read Full License
Human eye is an absolute sensory organ for vision. Eye sight is entirely accomplished by the blood flow in retinal vessels in eye. Diseases such as diabetes retinopathy, hypertension and arteriosclerosis cause change in branching pattern and diameter of retinal blood vessels leading to blindness. These changes can be analyzed by segmenting retinal blood vessel. Hence the retinal vasculature is recognized as the promising anatomical region for the diagnosis of several commonly seen diseases including cardiovascular related and diabetes. In this paper we propose two novel deep neural architectures named as Dilated fully convolved convolutional neural network (FCNN) and dilated depth concatenated neural network (DCNN) to segment the retinal blood vessels. The feature maps of fundus images are extracted by multiple dilated convolutional layers and due to the large field of view by dilation, pixel classification gets improved. The proposed work is evaluated for both the proposed architectures with and without dilation. It is observed from the obtained results that dilation enhances the network performance. To eliminate the non-uniform illumination and low contrast differences effect the preprocessed images are used for training the architectures. The proposed methodologies are experimented on the two publicly available databases DRIVE and STARE database.
Keywords
Retinal Color fundus images, deep neural network, dilation, Fully Convolved Neural Network, depth concatenation, Entropy Loss Function
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This preprint is under consideration at Wireless Personal Communications. A preprint is a preliminary version of a manuscript that has not completed peer review at a journal. Research Square does not conduct peer review prior to posting preprints. The posting of a preprint on this server should not be interpreted as an endorsement of its validity or suitability for dissemination as established information or for guiding clinical practice.
Learn more about In Review
Research Article
Sathananthavathi V
Mepco Schlenk Engineering College
Corresponding Author
ORCiD: https://orcid.org/0000-0002-8732-8652
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Under Review
Version 1
Posted 26 Apr, 2021
No community comments so far
Reviews received
Received 21 Apr, 2021
Editor assigned
On 07 Apr, 2021
First submitted
On 06 Apr, 2021
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
License:
This work is licensed under a CC BY 4.0 License. Read Full License
Human eye is an absolute sensory organ for vision. Eye sight is entirely accomplished by the blood flow in retinal vessels in eye. Diseases such as diabetes retinopathy, hypertension and arteriosclerosis cause change in branching pattern and diameter of retinal blood vessels leading to blindness. These changes can be analyzed by segmenting retinal blood vessel. Hence the retinal vasculature is recognized as the promising anatomical region for the diagnosis of several commonly seen diseases including cardiovascular related and diabetes. In this paper we propose two novel deep neural architectures named as Dilated fully convolved convolutional neural network (FCNN) and dilated depth concatenated neural network (DCNN) to segment the retinal blood vessels. The feature maps of fundus images are extracted by multiple dilated convolutional layers and due to the large field of view by dilation, pixel classification gets improved. The proposed work is evaluated for both the proposed architectures with and without dilation. It is observed from the obtained results that dilation enhances the network performance. To eliminate the non-uniform illumination and low contrast differences effect the preprocessed images are used for training the architectures. The proposed methodologies are experimented on the two publicly available databases DRIVE and STARE database.
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
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