Abstract
The long-lasting continuation of diabetes can cause damage to the nerves in the retina and may threaten the vision of the eye. These nerve lesions are ranked into micro aneurysms, hard exudates, hemorrhages, and cotton wool spots. Early-stage detection of lesions in the retina improves the betterment of successful treatments. Automatic detection of retinal lesions makes it easier for the ophthalmologists to analyze them without spending much time on manual segmentation. Image classification can be achieved by using atrous convolution method to improve the view of the lesions depending on the various lesions of retinopathy. The present technique includes the study of fundus images, normalization of shape, and normalization of size, segmentation and image classification. Atrous convolutional neural network is proposed to extract the features and classify the retinal lesions in fundus images. We achieved a classification accuracy of 0.944 which indicates the suitability of the proposed method in retinal lesion classification.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Kar S, Maity S (2018) automatic detection of retinal lesions for screening of diabetic retinopathy. IEEE Trans Biomed Eng 65(3):608–618
Mansour R (2017) Evolutionary computing enriched computer-aided diagnosis system for diabetic retinopathy: a survey. IEEE Rev Biomed Eng 10:334–349
Antal B, Hajdu A (2012) An ensemble-based system for microaneurysm detection and diabetic retinopathy grading. IEEE Trans Biomed Eng 59(6):1720–1726
Dai L, Fang R, Li H, Hou X, Sheng B, Wu Q, Jia W (2018) Clinical report guided retinal microaneurysm detection with multi-sieving deep learning. IEEE Trans Med Imaging 37(5):1149–1161
Gao Z, Li J, Guo J, Chen Y, Yi Z, Zhong J (2019) Diagnosis of diabetic retinopathy using deep neural networks. IEEE Access 7:3360–3370
Vijayalakshmi S, Dahiya S (2017) Medical image segmentation using various techniques: a survey. Int J Recent Trends Eng Res 3(2):120–130. https://doi.org/10.23883/ijrter.2017.3014.wni4f
Chen L, Papandreou G, Kokkinos I, Murphy K, Yuille A (2018) DeepLab: semantic image segmentation with deep convolutional nets, Atrous convolution, and fully connected CRFs. IEEE Trans Pattern Anal Mach Intell 40(4):834–848
Wisaeng K, Sa-Ngiamvibool W (2019) Exudates detection using morphology mean shift algorithm in retinal images. IEEE Access 7:11946–11958
Kumar D, Taylor G, Wong A (2019) Discovery radiomics with CLEAR-DR: interpretable computer aided diagnosis of diabetic retinopathy. IEEE Access 7:25891–25896
Imran A, Li J, Pei Y, Yang J, Wang Q (2019) Comparative analysis of vessel segmentation techniques in retinal images. IEEE Access 7:114862–114887
Seoud L, Hurtut T, Chelbi J, Cheriet F, Langlois J (2016) Red lesion detection using dynamic shape features for diabetic retinopathy screening. IEEE Trans Med Imaging 35(4):1116–1126
Imran A, et al (2019) Comparative analysis of vessel segmentation techniques in retinal images. IEEE Access 7:114862–114887. https://doi.org/10.1109/access.2019.2935912
Ieeexplore.ieee.org. 2020. Optic disc boundary and vessel origin segmentation of fundus images. IEEE J Mag [online]. Available at https://ieeexplore.ieee.org/document/7225107/. Accessed 13 June 2020@@@
Yang Y, Li T, Li W, Wu H, Fan W, Zhang W (2017) Lesion detection and grading of diabetic retinopathy via two-stages deep convolutional neural networks. Medical Image Comput Comput Assisted Intervention MICCAI 2017 Lecture Notes in Computer Science, pp. 533–540
Wang S, Yin Y, Cao G, Wei B, Zheng Y, Yang G (2017) Corrigendum to ‘hierarchical retinal blood vessel segmentation based on feature and ensemble learning’. Neurocomputing 149(2015):708–717. Neurocomputing 226:270–272
Rubini SS, Kunthavai A (2015) Diabetic retinopathy detection based on eigenvalues of the Hessian matrix. Procedia Comput Sci 47:311–318
Gadkari S, Maskati Q, Nayak B (2016) Prevalence of diabetic retinopathy in India: the all India ophthalmological society diabetic retinopathy eye screening study 2014. Indian J Ophthalmology 64(1):38
Quellec G, Charrière K, Boudi Y, Cochener B, Lamard M (2017) Deep image mining for diabetic retinopathy screening. Med Image Anal 39:178–193
Mookiah M, Acharya UR, Martis RJ, Chua CK, Lim C, Ng E, Laude A (2013) Evolutionary algorithm based classifier parameter tuning for automatic diabetic retinopathy grading: a hybrid feature extraction approach. Knowl Based Syst 39:9–22
Liang G, Hong H, Xie W, Zheng L (2018) Combining convolutional neural network with recursive neural network for blood cell image classification. In: IEEE access, vol 6, pp 36188–36197. https://doi.org/10.1109/ACCESS.2018.2846685
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Radha et al. (2022). Classification of Retinal Lesions in Fundus Images Using Atrous Convolutional Neural Network. In: Sivasubramanian, A., Shastry, P.N., Hong, P.C. (eds) Futuristic Communication and Network Technologies. VICFCNT 2020. Lecture Notes in Electrical Engineering, vol 792. Springer, Singapore. https://doi.org/10.1007/978-981-16-4625-6_55
Download citation
DOI: https://doi.org/10.1007/978-981-16-4625-6_55
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-4624-9
Online ISBN: 978-981-16-4625-6
eBook Packages: EngineeringEngineering (R0)