Abstract
Diabetic Retinopathy (DR) is a micro vascular complication caused by long-term diabetes mellitus. Unidentified diabetic retinopathy leads to permanent blindness. Early identification of this disease requires frequent complex diagnostic procedure which is expensive and time consuming. In this article, we propose a composite deep neural network architecture with gated-attention mechanism for automated diagnosis of diabetic retinopathy. The feature descriptors obtained from multiple pre-trained deep Convolutional Neural Networks (CNNs) are used to represent color fundus retinal images. Spatial pooling methods are introduced to get the reduced versions of these representations without loosing much information. The proposed composite DNN learns independently from each of these reduced representations through different channels and contributes to improving the model generalization. In addition, model also includes gated attention blocks which allows the model to emphasize more on lesion portions of the retinal images while reduced attention to the non-lesion regions. Our experiments on APTOS-2019 Kaggle blindness detection challenge reveal that, the proposed approach leads to improved performance when compared to the existing best models. Our empirical studies also reveal that, the proposed approach leads to more generalised predictions with multi-modal representations when compared to those of uni-modal representations. The proposed composite deep neural network model recorded an accuracy of 82.54% (\(\uparrow \) 2%), and a Kappa score of 79 (\(\uparrow 9\) points) for diabetic retinopathy severity level prediction.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Akram MU, Khalid S, Khan SA (2013) Identification and classification of microaneurysms for early detection of diabetic retinopathy. Pattern Recogn 46(1):107–116
Akram MU, Khalid S, Tariq A, Khan SA, Azam F (2014) Detection and classification of retinal lesions for grading of diabetic retinopathy. Comput Biol Med 45:161–171
APTOS 2019 Blindness Detection Challenge. (n.d.). https://www.kaggle.com/c/aptos2019-blindness-detection. Accessed 30 Dec 2019
Bhandary SV, Rao KA (2018) Automated screening system for retinal health using bi-dimensional empirical mode decomposition and integrated index. Comput Biol Med 75:54–62
Bodapati JD, Veeranjaneyulu N (2019) Facial emotion recognition using deep cnn based features. Int J Innov Technol Explor Eng
Bodapati JD, Veeranjaneyulu N, Shareef SN, Hakak S, Bilal M, Maddikunta PKR, Jo O (2020) Blended multi-modal deep convnet features for diabetic retinopathy severity prediction. Electronics 9(6):914
Casanova R, Saldana S, Chew EY, Danis RP, Greven CM, Ambrosius WT (2014) Application of random forests methods to diabetic retinopathy classification analyses. PLOS One 9(6):e98587
Cheung N, Rogers SL, Donaghue KC, Jenkins AJ, Tikellis G, Wong TY (2008) Retinal arteriolar dilation predicts retinopathy in adolescents with type 1 diabetes. Diabetes Care 31(9):1842–1846
Chollet F (2017) Xception: Deep learning with depthwise separable convolutions. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1251–1258
Deepika K, Bodapati JD, Srihitha RK (2019) An efficient automatic brain tumor classification using lbp features and svm-based classifier. In: Proceedings of international conference on computational intelligence and data engineering. Springer, pp 163–170
Deng J, Dong W, Socher R, Li L-J, Li K, Fei-Fei L (2009) Imagenet: A large-scale hierarchical image database. In: 2009 IEEE conference on computer vision and pattern recognition. IEEE, pp 248–255
Dondeti V, Bodapati JD, Shareef SN, Naralasetti V (2020) Deep convolution features in non-linear embedding space for fundus image classification deep convolution features in non-linear embedding space for fundus image classification. Revue d’Intelligence Artificielle 34(3):307–313
Dutta S, Manideep B, Basha SM, Caytiles RD, Iyengar N (2018) Classification of diabetic retinopathy images by using deep learning models. Int J Grid Distrib Comput 11(1):89–106
Grinsven M, Ginneken B, Hoyng C, Theelen T, Sanchez C (2016) Fast convolution neural network training using selective data sampling. IEEE Trans Med Imaging 35(5):1273–1284
Gulshan V, Peng L, Coram M, Stumpe MC, Wu D, Narayanaswamy A, Cuadros J et al (2016) Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA 316(22):2402–2410
Habib M, Welikala R, Hoppe A, Owen C, Rudnicka A, Barman S (2017) Detection of microaneurysms in retinal images using an ensemble classifier. Inform Med Unlock 9:44–57
Haloi M (2015) Improved microaneurysm detection using deep neural networks. arXiv preprint arXiv : 1505.04424
Haloi M, Dandapat S, Sinha R (2015) A gaussian scale space approach for exudates detection, classification and severity prediction. arXiv preprint arXiv:1505.00737
Jyostna Devi B, Nagur Shareef S, Veeranjaneyulu N, Mundukur NB (2020) Joint training of two-channel deep neural network for brain tumor classification. Signal Image Video Process
Kassani SH, Kassani PH, Khazaeinezhad R, Wesolowski MJ, Schneider KA, Deters R (2019) Diabetic retinopathy classification using a modified xception architecture. In: 2019 IEEEE international symposium on signal processing and information technology (isspit). IEEE, pp 1-6
Kaur N, Chatterjee S, Acharyya M, Kaur J, Kapoor N, Gupta S (2016) A supervised approach for automated detection of hemorrhages in retinal fundus images. In: 2016 5th international conference on wireless networks and embedded systems (wecon). IEEE, pp 1-5
Long S, Chen J, Hu A, Liu H, Chen Z, Zheng D (2020) Microaneurysms detection in color fundus images using machine learning based on directional local contrast. BioMed Eng OnLine 19:1–23
Long S, Huang X, Chen Z, Pardhan S, Zheng D (2019) Automatic detection of hard exudates in color retinal images using dynamic threshold and svm classification: Algorithm development and evaluation. BioMed research international, 2019
Mateen M, Wen J, Song S, Huang Z et al (2019) Fundus image classification using vgg-19 architecture with pca and svd. Symmetry 11(1):1
Mookiah MRK, Acharya UR, Martis RJ, Chua CK, Lim CM, 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
Noushin E, Pourreza M, Masoudi K, Ghiasi Shirazi E (2019) Microaneurysm detection in fundus images using a two step convolution neural network. Biomed Eng Online 18(1):67
Porter LF, Saptarshi N, Fang Y, Rathi S, Den Hollander AI, De Jong EK, Liloglou T et al (2019) Whole-genome methylation profiling of the retinal pigment epithelium of individuals with age-related macular degeneration reveals differential methylation of the ski, gtf2h4, and tnxb genes. Clin Epigenet 11(1):6
Rahim SS, Jayne C, Palade V, Shuttleworth J (2016) Automatic detection of microaneurysms in colour fundus images for diabetic retinopathy screening. Neural Comput Appl 27(5):1149–1164
Roychowdhury S, Koozekanani DD, Parhi KK (2013) Dream: diabetic retinopathy analysis using machine learning. IEEE J Biomed Health Inform 18(5):1717–1728
Shaban M, Ogur Z, Mahmoud A, Switala A, Shalaby A, Abu Khalifeh H, Sandhu H et al (2020) A convolutional neural network for the screening and staging of diabetic retinopathy. Plos One 15(6):e0233514
Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556
Srivastava R, Duan L, Wong DW, Liu J, Wong TY (2017) Detecting retinal microaneurysms and hemorrhages with robustness to the presence of blood vessels. Comput Methods Programs Biomed 138:83–91
Stitt AW, Curtis TM, Chen M, Medina RJ, McKay GJ, Jenkins A, Simo R et al (2016) The progress in understanding and treatment of diabetic retinopathy. Progress Retinal Eye Res 51:156–186
Verma K, Deep P, Ramakrishnan A (2011) Detection and classification of diabetic retinopathy using retinal images. In: 2011 annual IEEE india conference. IEEE, pp 1–6
Welikala RA, Roshan A, Fraz MM, Dehmeshki J, Hoppe A, Tah V, Mann S, Barman SA (2015) Genetic algorithm based feature selection combined with dual classification for the automated detection of proliferative diabetic retinopathy. Comput Med Imaging Graph 43:64–77
Welikala R, Dehmeshki J, Hoppe A, Tah V, Mann S, Williamson TH, Barman S (2014) Automated detection of proliferative diabetic retinopathy using a modified line operator and dual classification. Comput Methods Programs Biomed 114(3):247–261
Williams R, Airey M, Baxter H, Forrester J-M, Kennedy-Martin T, Girach A (2004) Epidemiology of diabetic retinopathy and macular oedema: a systematic review. Eye 18(10):963–983
Wu L, Fernandez-Loaiza P, Sauma J, Hernandez-Bogantes E, Masis M (2013) Classification of diabetic retinopathy and diabetic macular edema. World J Diabetes 4(6):290
Zeng X, Chen H, Luo Y, Ye W (2019) Automated diabetic retinopathy detection based on binocular siamese-like convolutional neural network. IEEE Access 7:30744–30753
Zoph B, Vasudevan V, Shlens J, Le QV (2018) Learning transferable architectures for scalable image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, 8697–8710
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. Composite deep neural network with gated-attention mechanism for diabetic retinopathy severity classification. J Ambient Intell Human Comput 12, 9825–9839 (2021). https://doi.org/10.1007/s12652-020-02727-z
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12652-020-02727-z