Abstract
Diabetic retinopathy (DR) is one of the main causes of loss of vision and blindness in humans across the world. DR is usually found in patients suffering from diabetes for a long period. Automation of DR diagnosis rescues many people from going blind by identifying the disease at the early stages. In this work, we introduce a robust model for DR severity level prediction by leveraging features extracted from pre-trained models to represent DR images. The activation filter values from multiple convolution blocks of VGG-16 are extracted and aggregated using pooling and fusion methods. The aggregation module produces a compact, informative, and discriminative representation of the retinal images by removing noisy and redundant features using pooling and fusion approaches. These feature representations are fed to the proposed DNN architecture to identify the severity level of DR. On the benchmark Kaggle APTOS 2019 contest dataset, our proposed method sets a new state-of-the-art result with an accuracy of 84.31% and an AUC 97. Experimental studies reveal that the proposed model exhibits superior performance compared with the existing models, especially in the case of severe and proliferate stage DR images.
Similar content being viewed by others
References
Aptos 2019. https://www.kaggle.com/c/aptos2019-blindness-detection. Accessed 30 Dec 2019 (2019)
Akram, M.U., Khalid, S., Khan, S.A.: Identification and classification of microaneurysms for early detection of diabetic retinopathy. Pattern Recognit. 46(1), 107–116 (2013)
Bodapati, J.D., Veeranjaneyulu, N.: Feature extraction and classification using deep convolutional neural networks. J. Cyber Secur. Mobil. 8(2), 261–276 (2019)
Bodapati, J.D., Veeranjaneyulu, N., Shareef, S.N., Hakak, S., Bilal, M., Maddikunta, P.K.R., Jo, O.: Blended multi-modal deep convnet features for diabetic retinopathy severity prediction. Electronics 9(6), 914 (2020)
Bodapati, J.D., Naralasetti, V.: Role of deep neural features vs hand crafted features for hand written digit recognition. Int. J. Recent Technol. Eng. 7, 147–152 (2019)
Bodapati, J.D., Naralasetti, V.: Facial emotion recognition using deep CNN based features. Int. J. Innov. Technol. Explor. Eng. 8, 1928–1931 (2019)
Casanova, R., Saldana, S., Chew, E.Y., Danis, R.P., Greven, C.M., Ambrosius, W.T.: Application of random forests methods to diabetic retinopathy classification analyses. PLoS ONE 9, 6 (2014)
Cheung, N., Rogers, S.L., Donaghue, K.C., Jenkins, A.J., Tikellis, G., Wong, T.Y.: Retinal arteriolar dilation predicts retinopathy in adolescents with type 1 diabetes. Diabetes Care 31(9), 1842–1846 (2008)
Flaxman, S., Bourne, R., Resnikoff, S., Ackland, P., Braithwaite, T., Cicinelli, M., Das, A., Jonas, J., Keeffe, J., Kempen, J., et al.: Global causes of blindness and distance vision impairment 1990–2020: a systematic review and meta-analysis. Lancet Glob. Health. 5(12), e1221-34 (2017)
García, G., Gallardo, J., Mauricio, A., López, J., Del Carpio, C.: Detection of diabetic retinopathy based on a convolutional neural network using retinal fundus images. In: International Conference on Artificial Neural Networks, pp. 635–642. Springer, Berlin (2017)
Gulshan, V., Peng, L., Coram, M., Stumpe, M.C., Wu, D., Narayanaswamy, A., Venugopalan, S., Widner, K., Madams, T., Cuadros, J., et al.: Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA 316(22), 2402–2410 (2016)
Habib, M., Welikala, R., Hoppe, A., Owen, C., Rudnicka, A., Barman, S.: Detection of microaneurysms in retinal images using an ensemble classifier. Inf. Med. Unlocked 9, 44–57 (2017)
Haloi, M., Dandapat, S., Sinha, R.: A gaussian scale space approach for exudates detection, classification and severity prediction. arXiv preprint arXiv:1505.00737 (2015)
Huan, E.-Y., Wen, G.-H.: Multilevel and multiscale feature aggregation in deep networks for facial constitution classification. Comput. Math. Methods Med. 2019, 1–11 (2019)
Jeong, D., Choo, S., Seo, W., Cho, N.I.: Regional deep feature aggregation for image retrieval. In: 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1737–1741. IEEE (2017)
Kassani, S.H., Kassani, P.H., Khazaeinezhad, R., Wesolowski, M.J., Schneider, K.A., Deters, R.: Diabetic retinopathy classification using a modified xception architecture. In: 2019 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT), pp. 1–6. IEEE (2017)
Lee, K.-S., Jung, S.-K., Ryu, J.-J., Shin, S.-W., Choi, J.: Evaluation of transfer learning with deep convolutional neural networks for screening osteoporosis in dental panoramic radiographs. J. Clin. Med. 9(2), 392 (2020)
Long, S., Huang, X., Chen, Z., Pardhan, S., Zheng, D.: Automatic detection of hard exudates in color retinal images using dynamic threshold and SVM classification: algorithm development and evaluation. BioMed Res. Int. 2019, 1–13 (2019)
Mansour, R.F.: Deep-learning-based automatic computer-aided diagnosis system for diabetic retinopathy. Biomed. Eng. Lett. 8(1), 41–57 (2018)
Mateen, M., Wen, J., Song, S., Huang, Z., et al.: Fundus image classification using VGG-19 architecture with PCA and SVD. Symmetry 11(1), 1 (2019)
Mookiah, M.R.K., Acharya, U.R., Martis, R.J., Chua, C.K., Lim, C.M., Ng, E., Laude, A.: Evolutionary algorithm based classifier parameter tuning for automatic diabetic retinopathy grading: a hybrid feature extraction approach. Knowl. Based Syst. 39, 9–22 (2013)
Noushin, E., Pourreza, M., Masoudi, K., Ghiasi Shirazi, E.: Microaneurysm detection in fundus images using a two step convolution neural network. Biomed. Eng. Online 18(1), 67 (2019)
Ozkan, S., Bozdagi Akar, G.: Relaxed spatio-temporal deep feature aggregation for real-fake expression prediction. In: Proceedings of the IEEE International Conference on Computer Vision Workshops, pp. 3094–3100 (2017)
Qureshi, I., Ma, J., Abbas, Q.: Recent development on detection methods for the diagnosis of diabetic retinopathy. Symmetry 11(6), 749 (2019)
Roychowdhury, S., Koozekanani, D.D., Parhi, K.K.: Dream: diabetic retinopathy analysis using machine learning. IEEE J. Biomed. Health Inform. 18(5), 1717–1728 (2013)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: ICLR (2015)
Srivastava, R., Duan, L., Wong, D.W., Liu, J., Wong, T.Y.: Detecting retinal microaneurysms and hemorrhages with robustness to the presence of blood vessels. Comput. Methods Programs Biomed. 138, 83–91 (2017)
Verma, K., Deep, P., Ramakrishnan, A.: Detection and classification of diabetic retinopathy using retinal images. In: 2011 Annual IEEE India Conference, pp. 1–6. IEEE (2011)
Wang, Q., Lai, J., Xu, K., Liu, W., Lei, L.: Beauty product image retrieval based on multi-feature fusion and feature aggregation. In: Proceedings of the 26th ACM International Conference on Multimedia, pp. 2063–2067 (2018)
Williams, R., Airey, M., Baxter, H., Forrester, J.-M., Kennedy-Martin, T., Girach, A.: Epidemiology of diabetic retinopathy and macular oedema: a systematic review. Eye 18(10), 963–983 (2004)
Wu, L., Fernandez-Loaiza, P., Sauma, J., Hernandez-Bogantes, E., Masis, M.: Classification of diabetic retinopathy and diabetic macular edema. World J. Diabetes 4(6), 290 (2013)
Yu, F., Wang, D., Shelhamer, E., Darrell, T.: Deep layer aggregation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2403–2412 (2018)
Zeng, X., Chen, H., Luo, Y., Ye, W.: Automated diabetic retinopathy detection based on binocular siamese-like convolutional neural network. IEEE Access 7, 30744–30753 (2019)
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Bodapati, J.D., Shaik, N.S. & Naralasetti, V. Deep convolution feature aggregation: an application to diabetic retinopathy severity level prediction. SIViP 15, 923–930 (2021). https://doi.org/10.1007/s11760-020-01816-y
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11760-020-01816-y