Automatic Diabetic Retinopathy Recognition Method based on GLDM Features and Feed Forward Neural Network Classifier.
Keywords:
: Retinopathy recognition; Retina Images; feed-forward neural network; GLDM; texture features.
Abstract
Detection and recognition of DR at the early phase can prevent the risk of gradual damage in the retina and vision loss. Many works have been introduced for automatic DR recognition and diagnosis in recent years. To date, there are still some issues that are required to work on to improve the quality and the performance of automatic DR recognition systems. Therefore, this paper introduces a machine learning based approach for DR diagnosis and recognition by proposing texture analysis features of GLDM technique and feed-forward neural network classifier. The proposed method has achieved a recognition accuracy of 95% according to undertaken experiments and performance analysis.
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References
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Pan, X., Jin, K., Cao, J., Liu, Z., Wu, J., You, K., . . . Jiang, J. (2020). Multi-label classification of retinal lesions in diabetic retinopathy for automatic analysis of fundus fluorescein angiography based on deep learning. Graefe's Archive for Clinical and Experimental Ophthalmology, 258(4), 779-785.
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Reed, T. R., & Dubuf, J. H. (1993). A review of recent texture segmentation and feature extraction techniques. CVGIP: Image understanding, 57(3), 359-372.
Sanderson, M., Bulloch, A. G., Wang, J., Williamson, T., & Patten, S. B. (2019). Predicting death by suicide using administrative health care system data: can feedforward neural network models improve upon logistic regression models?. Journal of affective disorders, 257, 741-747.
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Sharma, Hare Shyam and Singh, Ajit and Chandel, Amit Singh and Singh, Pravendra and Sapkal, Prof. Ashwini, Detection of Diabetic Retinopathy Using Convolutional Neural Network (May 17, 2019). Proceedings of International Conference on Communication and Information Processing (ICCIP) 2019, Available at SSRN: https://ssrn.com/abstract=3419210 or http://dx.doi.org/10.2139/ssrn.3419210.
Tavakoli, M., Jazani, S., & Nazar, M. (2020, March). Automated detection of microaneurysms in color fundus images using deep learning with different preprocessing approaches. In Medical Imaging 2020: Imaging Informatics for Healthcare, Research, and Applications (Vol. 11318, p. 113180E). International Society for Optics and Photonics.
Van Gool, L., Dewaele, P., & Oosterlinck, A. (1985). Texture analysis anno 1983. Computer vision, graphics, and image processing, 29(3), 336-357.
Ahmad, I. (2019). Automatic detection of diabetic retinopathy from fundus images using image processing and artificial neural network.
Ali KH, Tala EB, Alsaad NJ (2016) Texture features analysis using gray level co-occurrence matrix for a spine MRI images. International Journal of Computer Science and Information Security 14(9):666.
Alyoubi, W. L., Shalash, W. M., & Abulkhair, M. F. (2020). Diabetic retinopathy detection through deep learning techniques: A review. Informatics in Medicine Unlocked, 100377.
Amin, J., Sharif, M., Rehman, A., Raza, M., & Mufti, M. R. (2018). Diabetic retinopathy detection and classification using hybrid feature set. Microscopy research and technique, 81(9), 990-996.
Ashwin Dhakal, Laxmi Prasad Bastola, Subarna Shakya (2019). Detection and Classification of Diabetic Retinopathy Using Adaptive Boosting and Artificial Neural Network. International Journal of Advanced Research and Publications (IJARP), http://www.ijarp.org/online-papers-publishing/aug2019.html, Volume 3 - Issue 8, August 2019 Edition, 191-196 #ijarporg.
Bandara, A. M. R. R., & Giragama, P. W. G. R. M. P. B. (2017, December). A retinal image enhancement technique for blood vessel segmentation algorithm. In 2017 IEEE International Conference on Industrial and Information Systems (ICIIS) (pp. 1-5). IEEE.
Behera, M. K., & Chakravarty, S. (2020). Diabetic Retinopathy Image Classification Using Support Vector Machine. Paper presented at the 2020 International Conference on Computer Science, Engineering and Applications (ICCSEA).
Bourouis, S., Zaguia, A., & Bouguila, N. (2018, June). Hybrid statistical framework for diabetic retinopathy detection. In International Conference Image Analysis and Recognition (pp. 687-694). Springer, Cham.
Cao, P., Ren, F., Wan, C., Yang, J., & Zaiane, O. (2018). Efficient multi-kernel multi- instance learning using weakly supervised and imbalanced data for diabetic retinopathy diagnosis. Computerized Medical Imaging and Graphics, 69, 112-124.
Chevrefils, C., Chériet, F., Grimard, G., & Aubin, C. E. (2007, August). Watershed segmentation of intervertebral disk and spinal canal from MRI images. In International Conference Image Analysis and Recognition (pp. 1017-1027). Springer, Berlin, Heidelberg.
David, D. (2020). Retinal image classification system for diagnosis of diabetic retinopathy using morphological edge detection and feature extraction techniques. Artech J. Eff. Res. Eng. Technol, 1, 28-33.
Deepa, R., & Narayanan, N. (2020). Detection of microaneurysm in retina image using machine learning approach. Paper presented at the 2020 International Conference on Innovative Trends in Information Technology (ICITIIT).
Dutta, S., Manideep, B., Basha, S. M., Caytiles, R. D., & Iyengar, N. (2018). Classification of diabetic retinopathy images by using deep learning models. International Journal of Grid and Distributed Computing, 11(1), 89-106.
Ganesan, K., Martis, R. J., Acharya, U. R., Chua, C. K., Min, L. C., Ng, E. Y. K., & Laude, A. (2014). Computer aided diabetic retinopathy detection using trace transforms on digital fundus images. Medical & biological engineering & computing, 52(8), 663-672.
Gargeya, R., & Leng, T. (2017). Automated identification of diabetic retinopathy using deep learning. Ophthalmology, 124(7), 962-969.
Ghaffar, F., Khan, S., Uyyanonvara, B., Sinthanayothin, C., & Kaneko, H. (2020). Detection of Diabetic Anomalies in Retinal Images using Morphological Cascading Decision Tree. arXiv preprint arXiv:2001.01953.
GS, A. G. V., & Mohideen, K. (2017). Diabetic retinopathy screening system: A validation analysis with multiple fundus image databases. Biomedical Research (0970-938X), 28(4).
Hashia, B., & Mir, A. H. (2018). Texture features’ based classification of MR images of normal and herniated intervertebral discs. Multimedia Tools and Applications, 1-20.
Huang, F., Dashtbozorg, B., Zhang, J., Bekkers, E., Abbasi-Sureshjani, S., Berendschot, T. T., & ter Haar Romeny, B. M. (2016). Reliability of using retinal vascular fractal dimension as a biomarker in the diabetic retinopathy detection. Journal of ophthalmology, 2016.
Janney, B., Meera, G., Shankar, G. U., Divakaran, S., & Abraham, S. (2015). Detection and classification of exudates in retinal image using image processing techniques. Journal of Chemical and Pharmaceutical Sciences, 8, 541-546.
Kamble, V. V., & Kokate, R. D. (2020). Automated diabetic retinopathy detection using radial basis function. Procedia Computer Science, 167, 799-808.
Kaur, S., & Singh, D. (2018). Early Detection and Classification of Diabetic Retinopathy Using Empirical Transform and SVM. In Computational Vision and Bio Inspired Computing (pp. 1072-1083). Springer, Cham.
Li, Y.-H., Yeh, N.-N., Chen, S.-J., & Chung, Y.-C. (2019). Computer-assisted diagnosis for diabetic retinopathy based on fundus images using deep convolutional neural network. Mobile Information Systems, 2019.
Merlin, M., & Shan, B. P. (2015). Robust and efficient segmentation of blood vessel in retinal images using gray-level textures features and fuzzy SVM. Biomedical and Pharmacology Journal, 8(2), 1111-1120.
Pan, X., Jin, K., Cao, J., Liu, Z., Wu, J., You, K., . . . Jiang, J. (2020). Multi-label classification of retinal lesions in diabetic retinopathy for automatic analysis of fundus fluorescein angiography based on deep learning. Graefe's Archive for Clinical and Experimental Ophthalmology, 258(4), 779-785.
Rahim, S. S., Palade, V., & Holzinger, A. (2020). Image Processing and Machine Learning Techniques for Diabetic Retinopathy Detection: A Review Artificial Intelligence and Machine Learning for Digital Pathology (pp. 136-154): Springer.
Reed, T. R., & Dubuf, J. H. (1993). A review of recent texture segmentation and feature extraction techniques. CVGIP: Image understanding, 57(3), 359-372.
Sanderson, M., Bulloch, A. G., Wang, J., Williamson, T., & Patten, S. B. (2019). Predicting death by suicide using administrative health care system data: can feedforward neural network models improve upon logistic regression models?. Journal of affective disorders, 257, 741-747.
Shaharum, S. M., Hashim, N. H., Talip, N. A., Karim, M. S. A., & Faudzi, A. A. M. (2019). Automatic Detection of Diabetic Retinopathy Retinal Images Using Artificial Neural Network. In Proceedings of the 10th National Technical Seminar on Underwater System Technology 2018 (pp. 495-503). Springer, Singapore.
Sharma, Hare Shyam and Singh, Ajit and Chandel, Amit Singh and Singh, Pravendra and Sapkal, Prof. Ashwini, Detection of Diabetic Retinopathy Using Convolutional Neural Network (May 17, 2019). Proceedings of International Conference on Communication and Information Processing (ICCIP) 2019, Available at SSRN: https://ssrn.com/abstract=3419210 or http://dx.doi.org/10.2139/ssrn.3419210.
Tavakoli, M., Jazani, S., & Nazar, M. (2020, March). Automated detection of microaneurysms in color fundus images using deep learning with different preprocessing approaches. In Medical Imaging 2020: Imaging Informatics for Healthcare, Research, and Applications (Vol. 11318, p. 113180E). International Society for Optics and Photonics.
Van Gool, L., Dewaele, P., & Oosterlinck, A. (1985). Texture analysis anno 1983. Computer vision, graphics, and image processing, 29(3), 336-357.
Published
2022-03-08
How to Cite
talal, entesar, & Eman Thabet. (2022). Automatic Diabetic Retinopathy Recognition Method based on GLDM Features and Feed Forward Neural Network Classifier. Al-Qadisiyah Journal of Pure Science, 27(1), comp1-16. https://doi.org/10.29350/qjps.2022.27.1.1449
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Section
Computer
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