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Increasing the speed of diagnosis of glaucoma by using multitask deep neural network from retinal images | ||
| International Journal of Nonlinear Analysis and Applications | ||
| مقاله 22، دوره 16، شماره 4، تیر 2025، صفحه 263-270 اصل مقاله (1.87 M) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22075/ijnaa.2024.32688.4865 | ||
| نویسندگان | ||
| Manizheh Safarkhani Gargari1؛ MirHojjat Seyedi* 2؛ Mehdi Alilou3 | ||
| 1Department of Computer Science, Urmia Branch, Islamic Azad University, Urmia, Iran | ||
| 2Department of Biomedical Engineering, Urmia Branch, Islamic Azad University, Urmia, Iran | ||
| 3Department of Computer Science, Khoy Branch, Islamic Azad University, Khoy, Iran. | ||
| تاریخ دریافت: 25 آبان 1402، تاریخ بازنگری: 12 دی 1402، تاریخ پذیرش: 12 دی 1402 | ||
| چکیده | ||
| Glaucoma stands out as a prevalent ocular ailment in the elderly population, causing substantial harm to the optic nerves and eventual vision impairment. Fundus photography plays a pivotal role in the clinical assessment of glaucoma, facilitating the exploration of associated morphological alterations. Computational algorithms, capable of processing fundus images, have emerged as indispensable tools in this diagnostic domain. Hence, the imperative development of an automated diagnostic system leveraging image processing techniques is underscored. In this study, a novel approach to the segmentation and classification of retinal optic nerve head images is introduced. This method concurrently executes both tasks through a deep learning framework, thereby enhancing the learning speed within the network. The proposed network encompasses approximately 29 million parameters and demonstrates an efficiency of 2.5 seconds for segmenting and classifying retinal images. Central to this strategy is a multi-task deep learning network, harmonizing segmentation and classification processes, and leveraging information from both tasks to optimize learning efficacy. Validation of the proposed method is conducted using the publicly available ORIGA dataset. The attained performance metrics for accuracy, sensitivity, specificity, and F1-score are 99.461, 93.46, 100, and 98.7006, respectively. These results collectively affirm the substantial advancement achieved by the proposed method in comparison to existing methodologies. | ||
| کلیدواژهها | ||
| Deep learning؛ Convolutional Neural Network؛ Classification؛ Retinal Images؛ Disease Diagnosis؛ Multitasking Network | ||
| مراجع | ||
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[1] M. Abd Elaziz, E.O. Abo Zaid, M.A.A. Al-qaness, and R.A. Ibrahim, Automatic superpixel-based clustering for color image segmentation using q-generalized pareto distribution under linear normalization and hunger games search, Mathematics 9 (2021), no. 19. [2] L. Abdel-Hamid, Retinal image quality assessment using transfer learning: Spatial images vs. wavelet detail subbands, Ain Shams Eng. J. 12 (2021), no. 3, 2799–2807. [3] N. Akter, J. Fletcher, S. Perry, M.P. Simunovic, N. Briggs, and M. Roy, Glaucoma diagnosis using multi-feature analysis and a deep learning technique, Sci. Rep. 12 (2022), no. 1, 8064. [4] S. Chatterjee, A. Suman, R. Gaurav, S. Banerjee, A.K. Singh, B.K. Ghosh, R.K. Mandal, M. Biswas, and D. Maji, Retinal blood vessel segmentation using edge detection method, J. Phys. Conf. Ser. 1717 (2021), no. 1, 012008. [5] P.K. Chaudhary and R.B. Pachori, Automatic diagnosis of glaucoma using two-dimensional Fourier-Bessel series expansion based empirical wavelet transform, Biomed. Signal Process. Control 64 (2021), 102237. [6] S. Chen, Z. Wang, J. Shi, B. Liu, and N. Yu, A multi-task framework with feature passing module for skin lesion classification and segmentation, IEEE 15th Int. Symp. Biomed. Imag. (ISBI 2018), Washington, DC, IEEE, 2018, pp. 1126-1129. [7] F. Chollet, Xception: Deep learning with depthwise separable convolutions, in 2017 IEEE Conf. Comput. Vision and Pattern Recognition (CVPR), Honolulu, HI: IEEE (2017), 1800-1807. [8] S.A. David, C. Mahesh, V.D. Kumar, K. Polat, A. Alhudhaif, and M. Nour, Retinal blood vessels and optic disc segmentation using U-net, Math. Probl. Eng. 2022 (2022), no. 1, 1-11. [9] M. Deshpande, Introduction to Convolutional Neural Networks for Vision Tasks, [Online], 2017, Available: https://gamedevacademy.org/introduction-to-convolutional-neural-networks-for-vision-tasks/. [10] A. Diaz-Pinto, S. Morales, V. Naranjo, T. Kohler, J. M. Mossi, and A. Navea, CNNs for automatic glaucoma assessment using fundus images: An extensive validation, Biomed. Eng. Online 18 (2019), no. 1, 29. [11] Sh. Du, Understanding Dice Loss for Crisp Boundary Detection, AI salon [Online], 2020, Available: https: //medium.com/ai-salon/understanding-dice-loss-for-crisp-boundary-detection-bb30c2e5f62b. [12] M.S. Gargari, M.H. Seyedi, and M. Alilou, Segmentation of retinal blood vessels using U-net++ architecture and disease prediction, Electronics 11 (2022), no. 21. [13] S. Goel, S. Gupta, A. Panwar, S. Kumar, M. Verma, S. Bourouis, and M.A. Ullah, Deep learning approach for stages of severity classification in diabetic retinopathy using color fundus retinal images, Math. Probl. Eng. 2021 (2021), no. 1, 1-8. [14] I. K. Gupta, A. Choubey, and S. Choubey, Mayfly optimization with deep learning enabled retinal fundus image classification model, Comput. Electr. Eng. 102 (2022), 108176. [15] K. He, Z. Xiangyu, R. Shaoqing, and S. Jian, Deep Residual Learning for Image Recognition, Dec. 2015, [Online], Available: https://arxiv.org/pdf/1512.03385.pdf. [16] G. Huang, Z. Liu, L. Van Der Maaten, and K.Q. Weinberger, Densely connected convolutional networks, IEEE Conf. Comput. Vision Pattern Recog. (CVPR), Honolulu, HI: IEEE, 2017, pp. 2261-2269. [17] J. Kaur and P. Kaur, Automated computer-aided diagnosis of diabetic retinopathy based on segmentation and classification using K-nearest neighbor algorithm in retinal images, Comput. J. 66 (2023), no. 8, 2011–2032. [18] T. Khalil, M.U. Akram, H. Raja, A. Jameel and I. Basit, Detection of glaucoma using cup to disc ratio from spectral domain optical coherence tomography images, IEEE Access 6 (2018), 4560-4576. [19] Z. Kong, M. He, Q. Luo, X. Huang, P. Wei, Y Cheng, L Chen, Y. Liang, Y. Lu, X. Li, and J. Chen, Multi-task classification and segmentation for explicable capsule endoscopy diagnostics, Front. Mol. Biosci. 8 (2021), 614277. [20] T. L. T. Le, N. Thome, S. Bernard, V. Bismuth and F. Patoureaux, Multitask Classification and Segmentation for Cancer Diagnosis in Mammography, arXiv [Online], 2019, Available: http://arxiv.org/abs/1909.05397. [21] S. Maheshwari, V. Kanhangad and R. B. Pachori, CNN-Based Approach for Glaucoma Diagnosis Using Transfer Learning and LBP-Based Data Augmentation, arXiv [Online], 2020, Available: http://arxiv.org/abs/2002.08013. [22] R. Marques, D.A. De Jesus, J. Barbosa-Breda, J. Van Eijgen, I. Stalmans, T. van Walsum, S.tefan Klein, P.G. Vaz, and L. Sanchez Brea, Automatic segmentation of the optic nerve head region in optical coherence tomography: A methodological review, Comput. Methods Programs Biomed. 220 (2022), 106801. [23] F. Milletari, N. Navab, and S. A. Ahmadi, V-Net: Fully convolutional neural networks for volumetric medical image segmentation, 2016, doi: 10.48550/ARXIV.1606.04797. [24] B. Oliveira, H.R. Torres, P. Morais, F. Veloso, A.L. Baptista, J.C. Fonseca, and J.L. Vilaca, A multi-task convolutional neural network for classification and segmentation of chronic venous disorders, Sci. Rep. 13 (2023), no. 1, 761. [25] S. Pathan, P. Kumar, R.M. Pai, and S.V. Bhandary, Automated segmentation and classification of retinal features for glaucoma diagnosis, Biomed. Signal Process. Control 63 (2021), 102244. [26] A. Ramzan, M. Usman Akram, A. Shaukat, S. Gul Khawaja, U. Ullah Yasin, and W. Haider Butt, Automated glaucoma detection using retinal layers segmentation and optic cup-to-disc ratio in optical coherence tomography images, IET Image Process 13 (2019), no. 3, 409–420. [27] A. Rosebrock, Convolutional neural networks (CNNs) and layer types, [Online], 2021, Available: https: //pyimagesearch.com/2021/05/14/convolutional-neural-networks-cnns-and-layer-types/. [28] O. Ronneberger, P. Fischer, and T. Brox, U-Net: Convolutional Networks for Biomedical Image Segmentation, 18th Int. Conf., Munich, Germany, October 5-9, 2015, Proc., part III 18. Springer International Publishing, 2015. [29] T. Saba, S. Akbar, H. Kolivand, and S. Ali Bahaj, Automatic detection of papilledema through fundus retinal images using deep learning, Microsc. Res. Tech. 84 (2021), no. 12, 3066–3077. [30] J. Sahlsten, J. Jaskari, J. Kivinen, L. Turunen, E. Jaanio, K Hietala, and K. Kaski, Deep learning fundus image analysis for diabetic retinopathy and macular edema grading, Sci. Rep. 9 (2019), no. 1. [31] A. Sarhan, J. Rokne, and R. Alhajj, Glaucoma detection using image processing techniques: A literature review, Comput. Med. Imag. Graph. 78 (2019), 101657. [32] P. Sharma, VGG Network, Codingninjas, Apr. 21, 2023, https://www.codingninjas.com/codestudio/library/vggnet. [33] D. Shen, T. Liu, T.M. Peters, L.H. Staib, C. Essert, S. Zhou, P.T. Yap, and A. Khan, Medical image computing and computer assisted intervention-MICCAI 2019: 22nd International Conference, Shenzhen, China, October, Lecture Notes in Computer Science, 2019, pp. 13–17. [34] A. Shoukat, S. Akbar, S.A. Hassan, S. Iqbal, A. Mehmood, and Q.M. Ilyas, Automatic diagnosis of glaucoma from retinal images using deep learning approach, Diagnostics 13 (2023), no. 10, 1738. [35] K. Simonyan and A. Zisserman, Very Deep Convolutional Networks for Large-Scale Image Recognition, 2014, 10.48550/ARXIV.1409.1556. [36] M.B. Sudhan, M. Sinthuja, S. Pravinth Raja, J. Amutharaj, G. Charlyn Pushpa Latha, S. Sheeba Rachel, T. Anitha, T. Rajendran, and Y. Asrat Waji, Segmentation and classification of glaucoma using U-net with deep learning model, J. Healthc. Eng. 2022 (2022), 1–10. [37] C. Szegedy, W. Liu, Y. Jia, and P. Sermanet, Going deeper with convolutions, IEEE Conf. Comput. Vis. Pattern Recogn.(CVPR), Boston, MA, USA, IEEE, 2015, pp. 1–9. [38] R. Thanki, A deep neural network and machine learning approach for retinal fundus image classification, Healthc. Anal. 3 (2023), 100140. [39] B. Xu, N. Wang, T. Chen, and M. Li, Empirical evaluation of rectified activations in convolutional network, 2015, 10.48550/ARXIV.1505.00853. [40] M.Y.T. Yip, G. Lim, Z.W. Lim, Q.D. Nguyen, C.C.Y. Chong, M. Yu, V. Bellemo, Y. Xie, X.Q. Lee, H. Hamzah, and J. Ho, Technical and imaging factors influencing performance of deep learning systems for diabetic retinopathy, Npj Digit. Med. 3 (202), no. 1, 40. [41] K. Zervoudakis and S. Tsafarakis, A mayfly optimization algorithm, Comput. Ind. Eng. 145 (2020), 106559. [42] Zh. Zhang, F. Sh. Yin, J. Liu, W.K. Wong, N.M. Tan, B.H. Lee, J. Cheng, and T.Y. Wong., Origya-light: An online retinal fundus image database for glaucoma analysis and research, Ann. Int. Conf. IEEE Engin. Med. Bio., Buenos Aires, IEEE, 2021, pp. 3065–3068. | ||
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