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Title: :  Retinal Blood Vessel Segmentation using odd heavy U-net Architecture
PaperId: :  17127
Published in:   International Journal Of Advance Research And Innovative Ideas In Education
Publisher:   IJARIIE
e-ISSN:   2395-4396
Volume/Issue:    Volume 8 Issue 3 2022
DUI:    16.0415/IJARIIE-17127
Licence: :   IJARIIE is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Author NameAuthor Institute
Nagalla Vindhya sreeVasireddy Venkatadri Institute of Technology
Nagandla Hema LathaVasireddy Venkatadri Institute of Technology
T. VineelaVasireddy Venkatadri Institute of Technology
Kandula JayaSreeVasireddy Venkatadri Institute of Technology
Desaboina TejaSri Vasireddy Venkatadri Institute of Technology

Abstract

Electronics and Communication Engineering
Convolution neural networks, retinal blood vessels, ReLU, U-net, Odd heavy U-net
Blood vessel segmentation plays a vital role in computer aided diagnosis and treatment of retinal diseases. This is the reason why blood vessel segmentation has gained wide popularity among researchers. In this project we implement blood vessel segmentation based on an improved Odd heavy U-NET convolutional neural network (CNN) architecture. The architecture is very similar to U-net architecture only even layers have three convolutions followed by ReLU whereas odd layers have two convolutions followed by ReLU. The architecture consists of a contracting path to capture context and a symmetric expanding path that enables precise localization. Multiscale input layer and dense blocks are introduced into the conventional U-NET, so that the network can make use richer spatial context information. Especially for thin blood vessels, which are difficult to detect because of their low contrast with the background pixels, this segmentation results have been improved. This is the simplest architecture used for recognition of various retinal diseases. We show that such a network can be trained end to end from very few images and performs the prior best method. Further, the segmented outputs were able to cover thinner blood vessels better than previous methods aiding in early detection of pathologies.

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IJARIIE Nagalla Vindhya sree, Nagandla Hema Latha, T. Vineela, Kandula JayaSree, and Desaboina TejaSri . "Retinal Blood Vessel Segmentation using odd heavy U-net Architecture" International Journal Of Advance Research And Innovative Ideas In Education Volume 8 Issue 3 2022 Page 3091-3096
MLA Nagalla Vindhya sree, Nagandla Hema Latha, T. Vineela, Kandula JayaSree, and Desaboina TejaSri . "Retinal Blood Vessel Segmentation using odd heavy U-net Architecture." International Journal Of Advance Research And Innovative Ideas In Education 8.3(2022) : 3091-3096.
APA Nagalla Vindhya sree, Nagandla Hema Latha, T. Vineela, Kandula JayaSree, & Desaboina TejaSri . (2022). Retinal Blood Vessel Segmentation using odd heavy U-net Architecture. International Journal Of Advance Research And Innovative Ideas In Education, 8(3), 3091-3096.
Chicago Nagalla Vindhya sree, Nagandla Hema Latha, T. Vineela, Kandula JayaSree, and Desaboina TejaSri . "Retinal Blood Vessel Segmentation using odd heavy U-net Architecture." International Journal Of Advance Research And Innovative Ideas In Education 8, no. 3 (2022) : 3091-3096.
Oxford Nagalla Vindhya sree, Nagandla Hema Latha, T. Vineela, Kandula JayaSree, and Desaboina TejaSri . 'Retinal Blood Vessel Segmentation using odd heavy U-net Architecture', International Journal Of Advance Research And Innovative Ideas In Education, vol. 8, no. 3, 2022, p. 3091-3096. Available from IJARIIE, https://ijariie.com/AdminUploadPdf/Retinal_Blood_Vessel_Segmentation_using_odd_heavy_U_net_Architecture_ijariie17127.pdf (Accessed : ).
Harvard Nagalla Vindhya sree, Nagandla Hema Latha, T. Vineela, Kandula JayaSree, and Desaboina TejaSri . (2022) 'Retinal Blood Vessel Segmentation using odd heavy U-net Architecture', International Journal Of Advance Research And Innovative Ideas In Education, 8(3), pp. 3091-3096IJARIIE [Online]. Available at: https://ijariie.com/AdminUploadPdf/Retinal_Blood_Vessel_Segmentation_using_odd_heavy_U_net_Architecture_ijariie17127.pdf (Accessed : )
IEEE Nagalla Vindhya sree, Nagandla Hema Latha, T. Vineela, Kandula JayaSree, and Desaboina TejaSri , "Retinal Blood Vessel Segmentation using odd heavy U-net Architecture," International Journal Of Advance Research And Innovative Ideas In Education, vol. 8, no. 3, pp. 3091-3096, May-Jun 2022. [Online]. Available: https://ijariie.com/AdminUploadPdf/Retinal_Blood_Vessel_Segmentation_using_odd_heavy_U_net_Architecture_ijariie17127.pdf [Accessed : ].
Turabian Nagalla Vindhya sree, Nagandla Hema Latha, T. Vineela, Kandula JayaSree, and Desaboina TejaSri . "Retinal Blood Vessel Segmentation using odd heavy U-net Architecture." International Journal Of Advance Research And Innovative Ideas In Education [Online]. volume 8 number 3 ().
Vancouver Nagalla Vindhya sree, Nagandla Hema Latha, T. Vineela, Kandula JayaSree, and Desaboina TejaSri . Retinal Blood Vessel Segmentation using odd heavy U-net Architecture. International Journal Of Advance Research And Innovative Ideas In Education [Internet]. 2022 [Cited : ]; 8(3) : 3091-3096. Available from: https://ijariie.com/AdminUploadPdf/Retinal_Blood_Vessel_Segmentation_using_odd_heavy_U_net_Architecture_ijariie17127.pdf
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