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Detection of Cataract Based on Image Features Using Convolutional Neural Networks
https://doi.org/10.22146/ijccs.61882 Indra Weni(1), Pradita Eko Prasetyo Utomo(2*), Benedika Ferdian Hutabarat(3), Muksin Alfalah(4)
(1) Department of Information Systems, FST Universitas Jambi, Jambi
(2) Department of Information Systems, FST Universitas Jambi, Jambi
(3) Department of Information Systems, FST Universitas Jambi, Jambi
(4) Department of Information Systems, FST Universitas Jambi, Jambi
(*) Corresponding Author
Abstract
Cataract are the highest cause of blindness that there are 32.4 million people experiencing blindness and as many as 191 million people experiencing visual disabilities in 2010 in the world. On the other hand, the longer a patient suffers from cataracts or late treatment. The development of cataract identification using a traditional algorithm based on feature representation is highly dependent on the classification process carried out by an eye specialist so that the method is prone to misclassification of a person detected or not. However, at this time there is a deep learning, convolutional neural network (CNN) which is used for pattern recognition which can help automate image classification. This research was conducted to increase the accuracy value and minimize data loss in the process of cataract identification by performing an experience namely the manipulation process was carried out by changing epochs. The results of this study indicate that the addition of epochs affects accuracy and loss data from CNN. By comparing variety of epoch values it can be ignored that the higher the age values used, the higher the value of the model. In this study, using the epoch 50 value reached the highest value with a value of 95%. Based on the model that has been made it has also been successful to receive images according to the specified class. After testing accurately, 10 images achieved an average accuracy of 88%.
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[1] D. Pascolini and S. P. Mariotti, “Global estimates of visual impairment: 2010.” Br J Ophthalmol, vol. 96, no. 5, pp. 614–618, 2012.
[2] D. Allen and A. Vasavada, “Cataract and surgery for cataract.” Bmj, vol. 333, no. 7559, pp. 128–32, 2006
[3] A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in International Conference on Neural Information Processing Systems, 2012, pp. 1097–1105.
[4] R. Girshick, J. Donahue, T. Darrell, and J. Malik, “Rich feature hierarchies for accurate object detection and semantic segmentation,” in IEEE Conference on Computer Vision and Pattern Recognition, 2014, pp. 580–587
[5] E. P. I. W. Suartika, A. Y. Wijaya, and Soelaiman, R., “Image Classification Using Convolutional Neural Network (CNN) on Caltech 101”, Jurnal Teknik ITS Vol. 5, No. 1, 2016. ISSN: 2337-3539 (2301-9271 Print)
[6] E. S. Marquez, J. S. Hare, and M. Niranjan, “Deep Cascade Learning,” IEEE Transactions on Neural Networks and Learning Systems, vol.29, no. 11, pp. 5475-5485, November 2018. Available: https://ieeexplore.ieee.org/document/8307262.
[7] W. Rawat and Z. Wang, “Deep Convolutional Neural Networks for Image Classification: A Comprehensive Review,” Neural Computation, vol.29, no.9, pp. 2352- 2449, September 2017. Available: https://ieeexplore.ieee.org/document/8016501
[8] Wahyono and Hariyono, J. 2019. Determining Optimal Architecture of CNN using Genetic Algorithm for Vehicle Classification System. Indonesian Journal of Computing and Cybernetics Systems Vol.13, No.1, January 2019, pp. 63~72 ISSN (print): 1978-1520, ISSN (online): 2460-7258 DOI: https://doi.org/10.22146/ijccs.42299
[9] J. He and G. Lin, “Average Convergence Rate of Evolutionary Algorithms”, IEEE Transactions on Evolutionary Computation, vol.20, no.2, pp.316-321, 2016. Available: https://ieeexplore.ieee.org/document/7122298.
[10] D. Corus, D.-C. Dang, A. V. Eremeev, and P. K. Lehre, “Level-Based Analysis of Genetic Algorithms and Other Search Processes”. IEEE Transactions on Evolutionary Computation, vol.22, no.5, pp.707-719, 2018. Available: https://ieeexplore.ieee.org/document/8039236
[11] Y.-J. Gong, J.-J. Li, Y. Zhou, Y. Li, H. S.-H. Chung, Y.-H. Shi, and J. Zhang, “Genetic Learning Particle Swarm Optimization”, IEEE Transactions on Cybernetics, vol.46, no.10, pp. 2277-2290, 2016. Available: https://ieeexplore.ieee.org/document/7271066.
[12] Karamihan, K. C., Agustino, I. D. F., Bionesta, R. B. B., Tuason, F. C., Arellano, S. V. E., & Esguerra, P. A. M. (2019). SBC-Based cataract detection system using deep convolutional neural network with transfer learning algorithm. International Journal of Recent Technology and Engineering, 8(2), 4605–4613. https://doi.org/10.35940/ijrte.B3368.078219
[13] Sahana, M., & Gowrishankar, S. (2019). Identification and classification of cataract stages in old age people using deep learning algorithm. International Journal of Innovative Technology and Exploring Engineering, 8(10), 2767–2772. https://doi.org/10.35940/ijitee.J9582.0881019
[14] Zhang, L., Li, J., Zhang, I., Han, H., Liu, B., Yang, J., & Wang, Q. (2017). Automatic cataract detection and grading using Deep Convolutional Neural Network. Proceedings of the 2017 IEEE 14th International Conference on Networking, Sensing and Control, ICNSC 2017, 60–65. https://doi.org/10.1109/ICNSC.2017.8000068
[15] Khurana AK, Diseases of The Lens. 2007. Comprehensive Ophthalmology Fourth Edition. India : Newage International Publishers.2007 : 405
[16] Tana, L. 2006. Faktor Risiko dan Upaya Pencegahan Katarak Pada Kelompok Pekerja. Media Litbang Kesehatan Vol. XVI Nomor 1
[17] World Health Organization. 2010. Global Data On Visual Impairments 2010. Accesed http://www.who.int/blindness/G LOBALDATAFINALforweb.pd f?ua=1
[18] Rismiyati and SN. Azhari, “Convolutional Neural Network implementation for imagebased Salak sortation,” ICST (International Conference on Science and TechnologyComputer, 27-28 Oct. 2016 [Online]. Available:https://ieeexplore.ieee.org/document/7877351. [
[19] Juliansyah, L., Agustian, I dan Hadi, F., 2018. Deteksi Gangguan Ginjal Melaui Citra Digital Iris Mata Menggunakan Metode Convolutional Neural Network (CNN). Jurnal Amplifier Vol. 9 No. 1, Mei 2019 hal 1-9
[20] Milosevic, N. (2020). Introduction to Convolutional Neural Networks. In Introduction to Convolutional Neural Networks. https://doi.org/10.1007/978-1-4842-5648-0
[21] Nurhikmat, Triano. 2018. Implementasi Deep Learning Untuk Image Classification Menggunakan Algoritma Convolutonal Neural Network (CNN) Pada Citra Wayang Golek. Skripsi. Yogyakarta: Universitas Islam Indonesia.
[22] Dzulqarnain, M.F, Suprapto and Makhrus, F. 2019. Improvement of Convolutional Neural Network Accuracy on Salak Classification Based Quality on Digital Image. Indonesian Journal of Computing and Cybernetics Systems Vol.13, No.2, April 2019, pp. 189~198 ISSN (print): 1978-1520, ISSN (online): 2460-7258 DOI: 10.22146/ijccs.42036.
[23] S.R. Rupanagudi, G.B. Varsa, dan B.S. Ranjani, “A cost effective tomato maturity grading system using image processing for farmers,” IC3I (International Conference on Contemporary Computing and Informatics, 27-29 Nov. 2014 [Online]. Available: https://ieeexplore.ieee.org/document/7019591.
[24] R. C. Gonzalezand, and R. E. Woods, Digital Image Processing, Ed.3, New Jersey: Prentice Hall, 2008.
[25] Srivastava, N., Hinton, G., Krizhevsky, A., Sustkever, I., Salkhutdinov, R. 2014. Droput: A Simple Way to Prevent Neural Network from Overfitting. Journal of Machine Learning Research, 15(56), 1929-1958.
[26] Zhu, Q., He, Z., Zhang, T., Cui, W. 2020. Improving Classification Performance of Softmax Loss Function Based on Scalable Batch-Normalization. Appli.Scie.2020, 10,2950;DOI.10.3390/app10082590
DOI: https://doi.org/10.22146/ijccs.61882
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