Improving the Curvelet Saliency and Deep Convolutional Neural Networks for Diabetic Retinopathy Classification in Fundus Images
Received: 9 December 2021 | Revised: 28 December 2021 | Accepted: 29 December 2021 | Online: 12 February 2022
Corresponding author: N. T. Binh
Abstract
Retinal vessel images give a wide range of the abnormal pixels of patients. Therefore, classifying the diseases depending on fundus images is a popular approach. This paper proposes a new method to classify diabetic retinopathy in retinal blood vessel images based on curvelet saliency for segmentation. Our approach includes three periods: pre-processing of the quality of input images, calculating the saliency map based on curvelet coefficients, and classifying VGG16. To evaluate the results of the proposed method STARE and HRF datasets are used for testing with the Jaccard Index. The accuracy of the proposed method is about 98.42% and 97.96% with STARE and HRF datasets respectively.
Keywords:
saliency, VGG16, classification, retinal blood vessel, diabetic retinopathyDownloads
References
Y. Jiang, N. Tan, T. Peng, and H. Zhang, "Retinal Vessels Segmentation Based on Dilated Multi-Scale Convolutional Neural Network," IEEE Access, vol. 7, pp. 76342–76352, 2019. DOI: https://doi.org/10.1109/ACCESS.2019.2922365
S. Joshi and P. T. Karule, "A review on exudates detection methods for diabetic retinopathy," Biomedicine & Pharmacotherapy, vol. 97, pp. 1454–1460, Jan. 2018. DOI: https://doi.org/10.1016/j.biopha.2017.11.009
S. Kumar and B. Kumar, "Diabetic Retinopathy Detection by Extracting Area and Number of Microaneurysm from Colour Fundus Image," in 5th International Conference on Signal Processing and Integrated Networks, Noida, India, Feb. 2018, pp. 359–364. DOI: https://doi.org/10.1109/SPIN.2018.8474264
H. A. G. Priya, J. Anitha, D. E. Popescu, A. Asokan, D. J. Hemanth, and L. H. Son, "Detection and Grading of Diabetic Retinopathy in Retinal Images Using Deep Intelligent Systems: A Comprehensive Review," Computers, Materials & Continua, vol. 66, no. 3, pp. 2771–2786, 2021. DOI: https://doi.org/10.32604/cmc.2021.012907
C. Bhardwaj, S. Jain, and M. Sood, "Diabetic Retinopathy Lesion Discriminative Diagnostic System for Retinal Fundus Images," Advanced Biomedical Engineering, vol. 9, pp. 71–82, 2020. DOI: https://doi.org/10.14326/abe.9.71
N. T. Binh, V. T. H. Tuyet, N. M. Hien, and N. T. Thuy, "Retinal Vessels Segmentation by Improving Salient Region Combined with Sobel Operator Condition," in 6th International Conference on Future Data and Security Engineering, Nha Trang City, Vietnam, Nov. 2019, pp. 608–617. DOI: https://doi.org/10.1007/978-3-030-35653-8_39
Q. Yan et al., "Automated retinal lesion detection via image saliency analysis," Medical Physics, vol. 46, no. 10, pp. 4531–4544, 2019. DOI: https://doi.org/10.1002/mp.13746
N. S. Murthy and B. Arunadevi, "An effective technique for diabetic retinopathy using hybrid machine learning technique," Statistical Methods in Medical Research, vol. 30, no. 4, pp. 1042–1056, Dec. 2021. DOI: https://doi.org/10.1177/0962280220983541
A. A. Abdulsahib, M. A. Mahmoud, M. A. Mohammed, H. H. Rasheed, S. A. Mostafa, and M. S. Maashi, "Comprehensive review of retinal blood vessel segmentation and classification techniques: intelligent solutions for green computing in medical images, current challenges, open issues, and knowledge gaps in fundus medical images," Network Modeling Analysis in Health Informatics and Bioinformatics, vol. 10, no. 1, Nov. 2021, Art. no. 20. DOI: https://doi.org/10.1007/s13721-021-00294-7
M. T. Islam, H. R. H. Al-Absi, E. A. Ruagh, and T. Alam, "DiaNet: A Deep Learning Based Architecture to Diagnose Diabetes Using Retinal Images Only," IEEE Access, vol. 9, pp. 15686–15695, 2021. DOI: https://doi.org/10.1109/ACCESS.2021.3052477
H. A. Owida, A. Al-Ghraibah, and M. Altayeb, "Classification of Chest X-Ray Images using Wavelet and MFCC Features and Support Vector Machine Classifier," Engineering, Technology & Applied Science Research, vol. 11, no. 4, pp. 7296–7301, Aug. 2021. DOI: https://doi.org/10.48084/etasr.4123
N. Memari, A. R. Ramli, M. I. B. Saripan, S. Mashohor, and M. Moghbel, "Retinal Blood Vessel Segmentation by Using Matched Filtering and Fuzzy C-means Clustering with Integrated Level Set Method for Diabetic Retinopathy Assessment," Journal of Medical and Biological Engineering, vol. 39, no. 5, pp. 713–731, Jul. 2019.
B. Gharnali and S. Alipour, "MRI Image Segmentation Using Conditional Spatial FCM Based on Kernel-Induced Distance Measure," Engineering, Technology & Applied Science Research, vol. 8, no. 3, pp. 2985–2990, Jun. 2018. DOI: https://doi.org/10.48084/etasr.1999
W. Ding, Y. Sun, L. Ren, H. Ju, Z. Feng, and M. Li, "Multiple Lesions Detection of Fundus Images Based on Convolution Neural Network Algorithm With Improved SFLA," IEEE Access, vol. 8, pp. 97618–97631, 2020. DOI: https://doi.org/10.1109/ACCESS.2020.2996569
Y. Wang and S. Shan, "Accurate disease detection quantification of iris based retinal images using random implication image classifier technique," Microprocessors and Microsystems, vol. 80, Oct. 2021, Art. no. 103350. DOI: https://doi.org/10.1016/j.micpro.2020.103350
A. Krestanova, J. Kubicek, M. Penhaker, and J. Timkovic, "Premature infant blood vessel segmentation of retinal images based on hybrid method for the determination of tortuosity," Lékař a technika - Clinician and Technology, vol. 50, no. 2, pp. 49–57, Jun. 2020. DOI: https://doi.org/10.14311/CTJ.2020.2.02
F. Orujov, R. Maskeliunas, R. Damasevicius, and W. Wei, "Fuzzy based image edge detection algorithm for blood vessel detection in retinal images," Applied Soft Computing, vol. 94, Jun. 2020, Art. no. 106452. DOI: https://doi.org/10.1016/j.asoc.2020.106452
D. Maji and A. A. Sekh, "Automatic Grading of Retinal Blood Vessel in Deep Retinal Image Diagnosis," Journal of Medical Systems, vol. 44, no. 10, Jun. 2020, Art. no. 180. DOI: https://doi.org/10.1007/s10916-020-01635-1
K. Oh, H. M. Kang, D. Leem, H. Lee, K. Y. Seo, and S. Yoon, "Early detection of diabetic retinopathy based on deep learning and ultra-wide-field fundus images," Scientific Reports, vol. 11, no. 1, Jan. 2021, Art. no. 1897. DOI: https://doi.org/10.1038/s41598-021-81539-3
O. Noah Akande, O. Christiana Abikoye, A. Anthonia Kayode, and Y. Lamari, "Implementation of a Framework for Healthy and Diabetic Retinopathy Retinal Image Recognition," Scientifica, vol. 2020, May 2020, Art. no. e4972527. DOI: https://doi.org/10.1155/2020/4972527
A. D. Hoover, V. Kouznetsova, and M. Goldbaum, "Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response," IEEE Transactions on Medical Imaging, vol. 19, no. 3, pp. 203–210, Mar. 2000. DOI: https://doi.org/10.1109/42.845178
A. Budai, R. Bock, A. Maier, J. Hornegger, and G. Michelson, "Robust Vessel Segmentation in Fundus Images," International Journal of Biomedical Imaging, vol. 2013, Dec. 2013, Art. no. e154860. DOI: https://doi.org/10.1155/2013/154860
N. Memari, A. R. Ramli, M. I. B. Saripan, S. Mashohor, and M. Moghbel, "Retinal Blood Vessel Segmentation by Using Matched Filtering and Fuzzy C-means Clustering with Integrated Level Set Method for Diabetic Retinopathy Assessment," Journal of Medical and Biological Engineering, vol. 39, no. 5, pp. 713–731, Jul. 2019. DOI: https://doi.org/10.1007/s40846-018-0454-2
I. Atli and O. S. Gedik, "Sine-Net: A fully convolutional deep learning architecture for retinal blood vessel segmentation," Engineering Science and Technology, an International Journal, vol. 24, no. 2, pp. 271–283, Dec. 2021. DOI: https://doi.org/10.1016/j.jestch.2020.07.008
C. Suedumrong, K. Leksakul, P. Wattana, and P. Chaopaisarn, "Application of Deep Convolutional Neural Networks VGG-16 and GoogLeNet for Level Diabetic Retinopathy Detection," in Proceedings of the Future Technologies, Vancouver, BC, Canada, Nov. 2021, pp. 56–65. DOI: https://doi.org/10.1007/978-3-030-89880-9_5
M. Aatila, M. Lachgar, H. Hrimech, and A. Kartit, "Diabetic Retinopathy Classification Using ResNet50 and VGG-16 Pretrained Networks," International Journal of Computer Engineering and Data Science (IJCEDS), vol. 1, no. 1, pp. 1–7, Jul. 2021. DOI: https://doi.org/10.1155/2021/9979560
S. Xefteris, K. Tserpes, and T. Varvarigou, "A Method for Improving Renogram Production and Detection of Renal Pelvis using Mathematical Morphology on Scintigraphic Images," Engineering, Technology & Applied Science Research, vol. 2, no. 4, pp. 251–258, Aug. 2012. DOI: https://doi.org/10.48084/etasr.206
Downloads
How to Cite
License
Copyright (c) 2022 V. T. H. Tuyet, N. T. Binh, D. T. Tin
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain the copyright and grant the journal the right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) after its publication in ETASR with an acknowledgement of its initial publication in this journal.
