Diabetic Retinopathy Detection using Convolutional Neural Network: A Comparative Study on Different Architectures: doi: 10.14456/mijet.2021.8

Tassanee  Hattiya; Kwankamon Dittakan; Salang Musikasuwan

Authors

Tassanee Hattiya College of Computing, Prince of Songkla University, Phuket Campus
Kwankamon Dittakan Prince of Songkla University
Salang Musikasuwan Department of Mathematics and Computer Science, Faculty of Science and Technology, Prince of Songkla University, Pattani Campus

Keywords:

Diabetic Retinopathy detection, Image Analysis, Image Classification, Neural network, Convolutional neural network

Abstract

Diabetic retinopathy (DR) is a diabetes complication affects the eyes. The patients who lack treatments may be affected to the visual such as no clear vision, bleeding, or blindness. The problem of diabetic patients is the difficulty in the detection of DR until the symptom has happened. Early diagnosis is typically made using retina imagery obtained from the fundus camera. In this paper, an automated mechanism for DR screening is proposed. The idea is to construct a classifier that can be used to distinguish between DR or non-DR retina images. The fundamental idea is to segment retina images for obtaining the region of interest (ROI), while remaining compatible with the classification process. The ROI is then transformed into an appropriate format. It is suggested that the convolutional neural network (CNN) is the most classifier learning mechanism to be considered. With respect the work presented in this paper, seven convolutional neural network architectures have been applied to compare the classification performance: (i) AlexNet, (ii)ResNet50, (iii) DenseNet201, (iv) InceptionV3, (v) MobileNet, (vi) MnasNet and (vii) NASNetMobile. The process is fully described and evaluated. The data used for the evaluation was obtained from the Kaggle of 23,513 images. The best results were obtained from AlexNet learning mechanism with accuracy values of 98.42% and 81.32% for training and testing sets, respectively.

References

Habtamu Wondifraw Baynes. Classification, Pathophysiology, Diagnosis and Management of Diabetes Mellitus. Journals of Diabetes and Metabolism, 2015.

Association, A. D., et al. Diagnosis and classification of diabetes mellitus. Representation Diabetes Care, January 2013.

Avula Benjamin and Chandan Chakraborty. Detection of Hard Exudates in Retinal Fundus Images Using Deep Learning. In Proceedings of the 7th International Conference on Informatics, Electronics & Vision (ICIEV) and 2nd International Conference on Imaging, Vision & Pattern Recognition (icIVPR), pages 465-469, Kitakyushu, Ja-pan, February 2019.

Oliver Faust, Rajendra Acharya U., E. Y. K. Ng, Kwan-Hoong Ng and Jasjit S. Suri. Journal of Medical Systems, February 2012.

Victor Alves, Jose Neves, Moreira Maia and Luis Nelas. Medical image processing using ex-tended logic programming and artificial neural networks. In Proceedings of the International Symposium on VIPromCom Video/Image Processing and Multimedia Communications, pages 39-42, Zadar, Croatia, June 2002.

Akara Thammastitkul, and Bunyarit Uyyanonvara. Diabetic Retinopathy Stages Identification Using Retinal Images, In Proceedings of the Conference on Intelligent Computing, Computer Science & Information Systems (ICCSIS-16), Pattaya, Thailand, April 2016.

Dayong Wang, Aditya Khosla, Rishab Gargeya, Humayun Irshad and Andrew H. Beck. Deep Learning for Identifying Metastatic Breast Cancer, representation arXiv preprint arXiv:1606.05718, 2016.

Hany Kasban, Mohsen El-boundary and Dina Salama. A Comparative Study of Medical Imaging Techniques. International Journal of Information Science and Intelligent System. 4, April 2015

Sujittra Sa-ngiem, Kwankamon Dittakan, Kanya Temkiatvises, Sirisak Yaisoongnern and Kongkiat Kespechara, Cerebral Microbleed Detection by Extracting Area and Number from Susceptibility Weighted Imagery using Convolutional Neural Network, Journal of Physics: Conference Series, Volume 1229, pages 012038, May 2019.

Mark us Gudmundsson, Es-sam A. El-Kwae and Mansur R. Kabuka. Edge detec-tion in medical images using a genetic algorithm. IEEE Transactions on Medical Imaging, pages 469-474, June 1998.

FengLi Yu, Jing Sun, Annan Li, Jun Cheng, Cheng Wan and Jiang Liu. Image Quality Classification for DR Screening Using Deep Learning, In Proceedings of the 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pages 664-667, Seogwipo, South Korea, September 2017.

Wei Yang, Liming Zhong, Yang Chen, Liyan Lin, Zhentai Lu, Shupeng Liu, Yao Wu, Qianjin Feng and Wufan Chen. Predicting CT Image From MRI Data Through Feature Matching With Learned Nonlinear Local Descriptors, In IEEE Transactions on Medical Imaging, pages 977-987, January 2018.

Piyamas Suapang, Kobchai Dejhan and Surapun Yimmun. Medical Image Processing and Analysis for Nuclear Medicine Diagnosis, In International Conference on Control, Automation and Systems 2010, pages 2448-2451, Gyeonggi-do, South Korea, December 2010.

Euclides de Moraes Barros Jr, Michel Sehn, Aldo von Wangenheim and Dirk Krechel. In Proceedings of the IEEE Symposium on Computer-Based Medical Systems, pages 189-194, USA, August 2002

Asif Khan, Jian-Ping Lii and Riaz Ah-med Shaikw. Medical Image Processing using Fuzzy Logic In Proceedings of the 12th International Com-puter Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP), pages 163-167, Chengdu, China, June 216

Enrique V. Carrera, Andres Gonzalez and Ricardo Carrera. Automated detection of diabetic retinopathy using SVM. In Proceedings of the IEEE XXIV International Conference on Electronics, Electrical Engineering and Computing (INTERCON), pages 1-4, Cusco, Peru, October 2017.

Tanapat Ratanapakorn, Athi-wath Daengphoonphol, Nawapak Eua-Anant and Yosanan Yospaiboon. Digital image processing software for diagnosing diabetic retinopathy from fundus photographs. Journal of Clinical Ophthalmology 13, pages 641-648, 2019.

Dilip Singh Sisodia, Shruti Nair and Pooja Khobragade. Diaebtic Retinal Fundus Images: Preprocessing and Feature Extraction for Early Detection of Diabetic Retinopathy, Biomedical and Pharmacology Journal 10, pages 615-626, June 2017.

Sohini Roychowdhury, Stu-dent Member, Dara D. Koozekanani, and Keshab K. Parhi. DREAM: Diabetic Retinopathy Analysis Using Machine Learning, In IEEE journal of biomedical and health informatics 18, pages 1717-1728, December 2013.

Pilar Perez Conde, Jorge de la Calleja, Antonio Benitez and Ma. Auxilio Medina. Image-Based Classification of Diabetic Retinopathy using Machine Learning. In Proceedings of the 12th International Conference on Intelligent Systems Design and Applications (ISDA), pages 826-830, Kochi, India, January 2013.

Nikita Gurudath, Mehmet Celenk, and H. Bryan Riley. Machine Learning Identification of Diabetic Retinopathy from Fundus Images. In Proceedings of the IEEE Signal Processing in Medicine and Biology Symposium (SPMB). pages 1-7, Cusco, Peru, October 2017.

Kanungo, Y.S., Srinivasan, B., & Choudhary, S. Detecting diabetic retinopathy using deep learning. 2017 2nd IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT), pages 801-804, 2017.

Benzamin, A., & Chakraborty, C. Detection of Hard Exudates in Retinal Fundus Images Using Deep Learning. In 2018 Joint 7th International Conference on Informatics, Electronics & Vision (ICIEV) and 2018 2nd International Conference on Imaging, Vision & Pattern Recognition (icIVPR), pages 465-469, 2018.

Alex Krizhevsky, Ilya Sutskever and Geoffrey E. Hinton. ImageNet Classification with Deep Convolutional Neural Networks, In Advances in Neural Information Processing Systems, pages 1097-1105, January 2012.

Kaiming He, Xiangyu Zhang, Shaoqing Ren and Jian Sun. Deep Residual Learning for Image Recognition, In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pages 770-778, Las Vegas, NV, USA, December 2016.

Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K. Q. Densely connected convolutional networks. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 4700–4708, 2017.

Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V. & Rabinovich, A. Going deeper with convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition pp. 1–9, 2015.

Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z.Rethinking the inception architecture for computer vision. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 2818–2826, 2016.

Andrew G. Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto and Hartwig Adam. MobileNet: Efficient Convolutional Neural Network for Mobile Vision Application, representation arXiv:1704.04861, April 2017.

Zoph, B., Vasudevan, V., Shlens, J., and Le, Q. V. Learning transfer able architectures for scalable image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 8697–8710, 2018.