Multi‐level deep neural network for efficient segmentation of blood vessels in fundus images

Abstract Glaucoma is a retinal disease that damages the eye's optic nerve, frequently causing an irreversible loss of vision. However, the accurate diagnosis of this disease is difficult but early-stage diagnosis may cure this retinal disease. The objective of this research is to diagnose glaucoma disease in the top of the eye's optical nerve. The proposed approach detects glaucoma via four major steps namely Data enhancement phase, segmentation phase, feature extraction phase, and classification phase by the fractional gravitational search-based hybrid deep neural network (FGSA-HDNN) classifier. The proposed classifier is used for the exact classification of glaucoma infected images and normal images. Here, the proposed approach utilizes the statistical, textural, and vessel features from the segmented output. Also, the proposed FGSO algorithm is used for testing the deep neural network. From the experimental results, it is observed that the proposed glaucoma detection has obtained a sensitivity of 99.64%, a specificity of 97.84%, and an accuracy of 98.75% that outperforms other state-of-art methods.

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Deep neural network and random forest hybrid architecture for learning to detect retinal vessels in fundus images

2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) ◽

10.1109/embc.2015.7319030 ◽

2015 ◽

Cited By ~ 20

Author(s):

Debapriya Maji ◽

Anirban Santara ◽

Sambuddha Ghosh ◽

Debdoot Sheet ◽

Pabitra Mitra

Keyword(s):

Neural Network ◽

Random Forest ◽

Deep Neural Network ◽

Hybrid Architecture ◽

Retinal Vessels ◽

Fundus Images

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On-Chip Deep Neural Network Storage with Multi-Level eNVM

2018 55th ACM/ESDA/IEEE Design Automation Conference (DAC) ◽

10.1109/dac.2018.8465818 ◽

2018 ◽

Cited By ~ 1

Author(s):

Marco Donato ◽

Brandon Reagen ◽

Lillian Pentecost ◽

Udit Gupta ◽

David Brooks ◽

...

Keyword(s):

Neural Network ◽

Deep Neural Network ◽

Network Storage ◽

Multi Level ◽

On Chip

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A Multichannel Deep Neural Network for Retina Vessel Segmentation via a Fusion Mechanism

Frontiers in Bioengineering and Biotechnology ◽

10.3389/fbioe.2021.697915 ◽

2021 ◽

Vol 9 ◽

Author(s):

Jiaqi Ding ◽

Zehua Zhang ◽

Jijun Tang ◽

Fei Guo

Keyword(s):

Neural Network ◽

Blood Vessels ◽

High Performance ◽

Deep Neural Network ◽

High Efficiency ◽

Vessel Segmentation ◽

Prediction Probability ◽

Binary Segmentation ◽

Public Datasets ◽

Retina Vessel

Changes in fundus blood vessels reflect the occurrence of eye diseases, and from this, we can explore other physical diseases that cause fundus lesions, such as diabetes and hypertension complication. However, the existing computational methods lack high efficiency and precision segmentation for the vascular ends and thin retina vessels. It is important to construct a reliable and quantitative automatic diagnostic method for improving the diagnosis efficiency. In this study, we propose a multichannel deep neural network for retina vessel segmentation. First, we apply U-net on original and thin (or thick) vessels for multi-objective optimization for purposively training thick and thin vessels. Then, we design a specific fusion mechanism for combining three kinds of prediction probability maps into a final binary segmentation map. Experiments show that our method can effectively improve the segmentation performances of thin blood vessels and vascular ends. It outperforms many current excellent vessel segmentation methods on three public datasets. In particular, it is pretty impressive that we achieve the best F1-score of 0.8247 on the DRIVE dataset and 0.8239 on the STARE dataset. The findings of this study have the potential for the application in an automated retinal image analysis, and it may provide a new, general, and high-performance computing framework for image segmentation.

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