Hypertensive Retinopathy Detection in Fundus Images Using Deep Learning-Based Model - Shallow ConvNet
Journal of Ophthalmic and Optometric Sciences,
Vol. 5 No. 2 (2021),
14 December 2022
,
Page 21-30
https://doi.org/10.22037/joos.v5i2.39596
Abstract
Background: Hypertensive Retinopathy (HR) is amongst the abnormalities occurred with high blood pressure. This high blood pressure level makes retinal arterial narrower, retinal hemorrhages and cotton wool spots more harmful. Based on what was mentioned, early detection of hypertensive retinopathy is pivotal to prevent its following disabilities and boost its treatment with more accurate methods.
Material and Methods: The main objective of this study is to investigate an appropriate deep learning method for improving the automatic diagnosis of hypertensive retinopathy in its early stages. The complete data used in this study have been obtained from integration of Structured Analysis of the Retina (STARE) and The Digital Retinal Images for Vessel Extraction (DRIVE) datasets.
Results: Interestingly, we reached an accuracy of 87.5 % after using the well-suited preprocessing method to integrate different images for further analysis by our designed convolutional neural network (CNN).
Conclusion: This model performs well with integration of two mentioned datasets.
- Hypertensive Retinopathy
- Convolutional Neural Network
- Deep Learning
How to Cite
References
Writing Group M, Mozaffarian D, Benjamin EJ, Go AS, Arnett DK, Blaha MJ, et al. Executive Summary: Heart Disease and Stroke Statistics--2016 Update: A Report From the American Heart Association. Circulation. 2016;133(4):447-54.
Rosendorff C, Lackland DT, Allison M, Aronow WS, Black HR, Blumenthal RS, et al. Treatment of hypertension in patients with coronary artery disease: a scientific statement from the American Heart Association, American College of Cardiology, and American Society of Hypertension. Hypertension. 2015;65(6):1372-407.
Raghavendra U, Fujita H, Bhandary SV, Gudigar A, Tan JH, Acharya UR. Deep convolution neural network for accurate diagnosis of glaucoma using digital fundus images. Information Sciences. 2018;441:41-9.
Gamella-Pozuelo L, Fuentes-Calvo I, Gomez-Marcos MA, Recio-Rodriguez JI, Agudo-Conde C, Fernandez-Martin JL, et al. Plasma Cardiotrophin-1 as a Marker of Hypertension and Diabetes-Induced Target Organ Damage and Cardiovascular Risk. Medicine (Baltimore). 2015;94(30):e1218.
Wiharto, Suryani E. The review of computer aided diagnostic hypertensive retinopathy based on the retinal image processing. IOP Conference Series: Materials Science and Engineering. 2019;620(1):012099.
Abbas Q, Ibrahim MEA. DenseHyper: an automatic recognition system for detection of hypertensive retinopathy using dense features transform and deep-residual learning. Multimedia Tools and Applications. 2020;79(41):31595-623.
Triwijoyo BK, Pradipto YD. Detection of Hypertension Retinopathy Using Deep Learning and Boltzmann Machines. Journal of Physics: Conference Series. 2017;801:012039.
García-Floriano A, Ferreira-Santiago Á, Nieto OC, Yáñez-Márquez C. A machine learning approach to medical image classification: Detecting age-related macular degeneration in fundus images. Comput Electr Eng. 2019;75:218-29.
Asiri N, Hussain M, Al Adel F, Alzaidi N. Deep learning based computer-aided diagnosis systems for diabetic retinopathy: A survey. Artif Intell Med. 2019;99:101701.
Abbasi K, Razzaghi P, Poso A, Ghanbari-Ara S, Masoudi-Nejad A. Deep learning in drug target interaction prediction: current and future perspectives. Current Medicinal Chemistry. 2021;28(11):2100-13.
Hooshmand SA, Zarei Ghobadi M, Hooshmand SE, Azimzadeh Jamalkandi S, Alavi SM, Masoudi-Nejad A. A multimodal deep learning-based drug repurposing approach for treatment of COVID-19. Molecular diversity. 2021;25:1717-30.
LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521(7553):436-44.
Hong Z, editor A preliminary study on artificial neural network. 2011 6th IEEE Joint International Information Technology and Artificial Intelligence Conference; 2011 20-22 Aug. 2011.
Fukushima K. Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biological Cybernetics. 1980;36(4):193-202.
Kim Y, editor Convolutional Neural Networks for Sentence Classification2014 October; Doha, Qatar: Association for Computational Linguistics.
Zhou X, Gong W, Fu W, Du F, editors. Application of deep learning in object detection. 2017 IEEE/ACIS 16th International Conference on Computer and Information Science (ICIS); 2017 24-26 .
Ranjan R, Sankaranarayanan S, Bansal A, Bodla N, Chen JC, Patel VM, et al. Deep Learning for Understanding Faces: Machines May Be Just as Good, or Better, than Humans. IEEE Signal Processing Magazine. 2018;35(1):66-83.
Druzhkov PN, Kustikova VD. A survey of deep learning methods and software tools for image classification and object detection. Pattern Recognition and Image Analysis. 2016;26(1):9-15.
Milyaev S, Laptev I. Towards reliable object detection in noisy images. Pattern Recognition and Image Analysis. 2017;27(4):713-22.
Patil A, Rane M, editors. Convolutional Neural Networks: An Overview and Its Applications in Pattern Recognition. Information and Communication Technology for Intelligent Systems; 2021 2021//; Singapore: Springer Singapore.
Mostafa S, Wu F-X. Diagnosis of autism spectrum disorder with convolutional autoencoder and structural MRI images. 2021. p. 23-38.
Sarvamangala DR, Kulkarni RV. Convolutional neural networks in medical image understanding: a survey. Evolutionary Intelligence. 2022;15(1):1-22.
Hoover AD, Kouznetsova V, Goldbaum M. Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response. IEEE Transactions on Medical Imaging. 2000;19(3):203-10.
Staal J, Abramoff MD, Niemeijer M, Viergever MA, Ginneken Bv. Ridge-based vessel segmentation in color images of the retina. IEEE Transactions on Medical Imaging. 2004;23(4):501-9.
Chen C, Wei J, Peng C, Zhang W, Qin H. Improved Saliency Detection in RGB-D Images Using Two-Phase Depth Estimation and Selective Deep Fusion. IEEE Transactions on Image Processing. 2020;29:4296-307.
Deng J, Dong W, Socher R, Li LJ, Kai L, Li F-F, editors. ImageNet: A large-scale hierarchical image database. 2009 IEEE Conference on Computer Vision and Pattern Recognition; 2009 20-25 June 2009.
Akbar S, Akram MU, Sharif M, Tariq A, Yasin Uu. Arteriovenous ratio and papilledema based hybrid decision support system for detection and grading of hypertensive retinopathy. Computer Methods and Programs in Biomedicine. 2018;154:123-41.
Cavallari M, Stamile C, Umeton R, Calimeri F, Orzi F. Novel Method for Automated Analysis of Retinal Images: Results in Subjects with Hypertensive Retinopathy and CADASIL. Biomed Res Int. 2015;2015:752957.
- Abstract Viewed: 69 times
- pdf Downloaded: 45 times