Abstract
Diabetic retinopathy (DR) is a prevailing disease that causes blindness among diabetic patients. The timely intervention with the regular fundus photography screening is the efficient way to cope with this disease. Screening of a large number of diabetic patients urges to the computer-aided and fully automatic DR diagnosis. Deep neural networks are gaining more attention due to their effectiveness in various tasks. The diagnosis of the can be made automatic and accurate suggestions can be provided to DR patients. The classification of DR images is challenging and important step. Therefore, in this paper, we have proposed a learning-based DR image reconstruction followed by a classification approach for DR image classification. Initially, the learning-based image reconstruction approach is proposed with a multi-encoder decoder network and residual inception block to delve into the features of input medical image and convert it into a set of abstract features followed by the reconstruction of the input medical image. The features from encoder are the abstract version of actual image representation which can be used for robust reconstruction for the input image. Therefore, these encoded features are used for DR image classification. The results analysis of the proposed framework with existing SOTA feature extraction algorithms is conducted on the MESSIDOR database for DR image classification. From the results’ analysis, it is evident that the proposed reconstruction-based classification framework outperforms the existing SOTA feature extraction algorithms.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
LeCun, Y., et al.: Gradient-based learning applied to document recognition. In: Proceedings of the IEEE 86.11, pp. 2278–2324 (1998)
Tan, X., Triggs, B.: Enhanced local texture feature sets for face recognition under difficult lighting conditions. IEEE Trans. Image Process. 19(6), 1635–1650 (2010)
Patil Prashant, W., Dudhane, A., Kulkarni, A., Murala, S., Gonde, A.B., Gupta, S.: An unified recurrent video object segmentation framework for various surveillance environments. IEEE Trans. Image Proc. 30, 7889–7902 (2021)
Amin, J., et al.: A method for the detection and classification of diabetic retinopathy using structural predictors of bright lesions. J. Comput. Sci. 19, 153–164 (2017)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition (2014). arXiv preprint arXiv:1409.1556
Sikder, N., et al.: Severity classification of diabetic Retinopathy using an ensemble learning algorithm through analyzing retinal Images. Symmetry 13(4), 670 (2021)
Patil Prashant, W., Biradar, K.M., Dudhane, A., Murala, S.: An end-to-end edge aggregation network for moving object segmentation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8149–8158 (2020)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Adv. Neural. Inf. Process. Syst. 25, 1097–1105 (2012)
Alyoubi, W.L., Abulkhair, M.F., Shalash, W.M.: Diabetic Retinopathy fundus Image classification and lesions localization system using deep learning. Sensors 21(11), 3704 (2021)
Bakator, M., Radosav, D.: Deep learning and medical diagnosis: a review of literature. Multi. Technol. Interact. 2(3), 47 (2018)
Patil, P.W., Murala, S.: Msfgnet: A novel compact end-to-end deep network for moving object detection. IEEE Trans. Intell. Transp. Syst. 20(11), 4066–4077 (2018)
Galshetwar, G.M., et al.: Local directional gradient based feature learning for image retrieval. In: 2018 IEEE 13th International Conference on Industrial and Information Systems (ICIIS). IEEE (2018)
Mookiah, M.R.K., et al.: Evolutionary algorithm based classifier parameter tuning for automatic diabetic retinopathy grading: a hybrid feature extraction approach. Knowl. Based Syst. 39, 9–22 (2013)
Hambarde, P., Dudhane, A., Patil, P.W., Murala, S., Dhall, A.: Depth estimation from single image and semantic prior. In: 2020 IEEE International Conference on Image Processing (ICIP), pp. 1441–1445. IEEE (2020)
Ophthalmoscopy, Dilated, ETDRS Levels: International clinical diabetic retinopathy disease severity scale detailed table (2002)
Akshay, D., Hambarde, P., Patil, P., Murala, S.: Deep underwater image restoration and beyond. IEEE Signal Process. Lett. 27, 675–679 (2020)
Rubini, S.S., Kunthavai, A.: Diabetic retinopathy detection based on eigenvalues of the hessian matrix. Proc. Comput. Sci. 47, 311–318 (2015)
Alyoubi, W.L., Shalash, W.M., Abulkhair, M.F.: Diabetic retinopathy detection through deep learning techniques: a review. Informatics in Medicine Unlocked 20, 100377 (2020)
Patil, P.W., Dudhane, A., Chaudhary, S., Murala, S.: Multi-frame based adversarial learning approach for video surveillance. Pattern Recogn. 122, 108350 (2022)
Bhatkar, A.P., Kharat, G.U.: Detection of diabetic retinopathy in retinal images using MLP classifier. In: 2015 IEEE International Symposium on Nanoelectronic and Information Systems. IEEE (2015)
Gonde, A.B., et al.: Volumetric local directional triplet patterns for biomedical image retrieval. In: 2017 Fourth International Conference on Image Information Processing (ICIIP). IEEE (2017)
Murala, S., Jonathan Wu, Q.M.: Spherical symmetric 3D local ternary patterns for natural, texture and biomedical image indexing and retrieval. Neurocomputing 149, 1502–1514 (2015)
Decencière, E., et al.: Feedback on a publicly distributed image database: the Messidor database. Image Anal. Stereology 33(3), 231–234 (2014)
Phutke, S.S., Murala, S.: Diverse receptive field based adversarial concurrent encoder network for image inpainting. IEEE Signal Process. Lett. 28, 1873–1877 (2021)
Hua, C.-H., et al.: Convolutional network with twofold feature augmentation for diabetic retinopathy recognition from multi-modal images. IEEE J. Biomed. Health Inf. 25(7), 2686–2697 (2020)
Quellec, G., et al.: Deep image mining for diabetic retinopathy screening. Med. Image Anal. 39, 178–193 (2017)
Yang, Y., Li, T., Li, W., Wu, H., Fan, W., Zhang, W.: Lesion detection and grading of diabetic retinopathy via two-stages deep convolutional neural networks. In: Descoteaux, M., Maier-Hein, L., Franz, A., Jannin, P., Collins, D.L., Duchesne, S. (eds.) MICCAI 2017. LNCS, vol. 10435, pp. 533–540. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-66179-7_61
Kulkarni, A., Patil, P.W., Murala, S.: Progressive subtractive recurrent lightweight network for video deraining. IEEE Signal Process. Lett. 29, 229–233 (2022). https://doi.org/10.1109/LSP.2021.3134171
Chandore, V., Asati, S.: Automatic detection of diabetic retinopathy using deep convolutional neural network. Int. J. Adv. Res. Ideas Innov. Technol. 3, 633–641 (2017)
Satpathy, A., Jiang, X., Eng, H.-L.: LBP-based edge-texture features for object recognition. IEEE Trans. Image Process. 23(5), 1953–1964 (2014)
Murala, S., Maheshwari, R.P., Balasubramanian, R.: Local tetra patterns: a new feature descriptor for content-based image retrieval. IEEE Trans. Image Proc. 21(5), 2874–2886 (2012)
Prasad, D.K., Vibha, L., Venugopal, K.R.: Early detection of diabetic retinopathy from digital retinal fundus images. In: 2015 IEEE Recent Advances in Intelligent Computational Systems (RAICS). IEEE (2015)
Ojala, T., Pietikäinen, M., Harwood, D.: A comparative study of texture measures with classification based on featured distributions. Pattern Recogn. 29(1), 51–59 (1996)
Resnikoff, S., et al.: The number of ophthalmologists in practice and training worldwide: a growing gap despite more than 200 000 practitioners. British J. Ophthalmol. 96(6), 783–787 (2012)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Deshmukh, P., Gaikwad, A.N. (2022). Deep Learning Based DR Medical Image Classification. In: Raman, B., Murala, S., Chowdhury, A., Dhall, A., Goyal, P. (eds) Computer Vision and Image Processing. CVIP 2021. Communications in Computer and Information Science, vol 1568. Springer, Cham. https://doi.org/10.1007/978-3-031-11349-9_41
Download citation
DOI: https://doi.org/10.1007/978-3-031-11349-9_41
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-11348-2
Online ISBN: 978-3-031-11349-9
eBook Packages: Computer ScienceComputer Science (R0)