Abstract
Retinal vessel segmentation segmentizes the blood vessels from retinal fundus images; this helps detect retinal diseases. Normalization techniques such as group normalization, layer normalization, and instance normalization were introduced to replace batch normalization. This paper evaluates the performance of these normalization techniques in a convolutional neural network (CNN) on retinal vessel segmentation: how it helps in improving the generalization ability of the model. The digital retinal images for vessel extraction (DRIVE), a publicly available dataset, are used for this experiment. Accuracy, F1 score, and Jaccard index of models with these normalization techniques were calculated. By empirical experiments, it is observed that the batch normalization outperforms its peers in CNN in terms of its accuracy. However, group normalization gives better convergence than other normalization techniques in terms of the validation error and results in a better generalized architecture for this segmentation task.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Atli I, Gedik OS (2021) Sine-Net: a fully convolutional deep learning architecture for retinal blood vessel segmentation. Eng Sci Technol Int J 24(2):271–283
Ba JL, Kiros JR, Hinton GE (2016) Layer normalization. arXiv preprint arXiv:1607.06450
Balestriero R, Bottou L, LeCun Y (2022) The effects of regularization and data augmentation are class dependent. arXiv preprint arXiv:2204.03632
Chen C, Chuah JH, Raza A, Wang Y (2021) Retinal vessel segmentation using deep learning: a review. IEEE Access
Dong H, Zhang T, Zhang T, Wei L (2022) Supervised learning-based retinal vascular segmentation by M-UNet full convolutional neural network. In: Signal, image & video processing, pp 1–7
Garbin C, Zhu X, Marques O (2020) Dropout vs. batch normalization: an empirical study of their impact to deep learning. Multimedia Tools Appl 79(19):12777–12815
Guo T, Dong J, Li H, Gao Y (2017) Simple convolutional neural network on image classification. In: Proceedings of the IEEE 2nd International Conference Big Data Analysis (ICBDA. IEEE, pp 721–724
Hakim L, Kavitha MS, Yudistira N, Kurita T (2021) Regularizer based on euler characteristic for retinal blood vessel segmentation. Pattern Recogn Lett 149:83–90
Ioffe S, Szegedy C (2015) Batch normalization: accelerating deep network training by reducing internal covariate shift. In: Proceedings of the International Conference Machine Learning (ICML), PMLR, pp 448–456
Jifara W, Jiang F, Rho S, Cheng M, Liu S (2019) Medical image denoising using convolutional neural network: a residual learning approach. J Supercomput 75(2):704–718
Murugan R, Roy P (2022) MicroNet: microaneurysm detection in retinal fundus images using convolutional neural network. In: Soft computing, pp 1–10
Myronenko A (2018) 3D MRI brain tumor segmentation using autoencoder regularization. In: Proceedings of the international MICCAI Brainlesion workshop. Springer, Heidelberg, pp 311–320
Ronneberger O, Fischer P, Brox T (2015) U-net: convolutional networks for biomedical image segmentation. In: Proceedings of the international conference on medical image computing and computer-assisted intervention. Springer, Heidelberg, pp 234–241
Saranya P, Prabakaran S, Kumar R, Das E (2021) Blood vessel segmentation in retinal fundus images for proliferative diabetic retinopathy screening using deep learning. In: The visual computer, pp 1–16
Sarvamangala D, Kulkarni RV (2021) Convolutional neural networks in medical image understanding: a survey. In: Evolutionary intelligence, pp 1–22
Soomro TA, Afifi AJ, Gao J, Hellwich O, Paul M, Zheng L (2018) Strided U-Net model: retinal vessels segmentation using dice loss. In: Digital Image Computing: Techniques and Applications (DICTA). IEEE, pp 1–8
Staal J, Abràmoff MD, Niemeijer M, Viergever MA, Van Ginneken B (2004) Ridge-based vessel segmentation in color images of the retina. IEEE Trans Med Imaging 23(4):501–509
Thanapol P, Lavangnananda K, Bouvry P, Pinel F, Leprévost F (2020) Reducing overfitting and improving generalization in training convolutional neural network under limited sample sizes in image recognition. In: Proceedings of the 5th International Conference on Information Technology (InCIT). IEEE, pp 300–305
Ulyanov D, Vedaldi A, Lempitsky V (2016) Instance normalization: the missing ingredient for fast stylization. arXiv preprint arXiv:1607.08022
Wang C, Zhao Z, Yu Y (2021) Fine retinal vessel segmentation by combining Nest U-net and patch-learning. Soft Comput 25(7):5519–5532
Wu Y, He K (2018) Group normalization. In: Proceedings of the European Conference on Computer Vision (ECCV), pp 3–19
Xiancheng W, Wei L, Bingyi M, He J, Jiang Z, Xu W, Ji Z, Hong G, Zhaomeng S (2018) Retina blood vessel segmentation using a u-net based convolutional neural network. In: Procedia Computer Science: Proceedings of the International Conference Data Science (ICDS 2018), pp 8–9
Zhang C, Bengio S, Hardt M, Recht B, Vinyals O (2021) Understanding deep learning (still) requires rethinking generalization. Commun ACM 64(3):107–115
Zheng Q, Yang M, Yang J, Zhang Q, Zhang X (2018) Improvement of generalization ability of deep CNN via implicit regularization in two-stage training process. IEEE Access 6:15844–15869
Acknowledgment
We thank the anonymous reviewers for their valuable feedback by which the readability of the paper is improved.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Kundalakkaadan, J., Rawat, A., Kumar, R. (2023). Performance Assessment of Normalization in CNN with Retinal Image Segmentation. In: Thakur, M., Agnihotri, S., Rajpurohit, B.S., Pant, M., Deep, K., Nagar, A.K. (eds) Soft Computing for Problem Solving. Lecture Notes in Networks and Systems, vol 547. Springer, Singapore. https://doi.org/10.1007/978-981-19-6525-8_13
Download citation
DOI: https://doi.org/10.1007/978-981-19-6525-8_13
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-19-6524-1
Online ISBN: 978-981-19-6525-8
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)