Diabetic Retinopathy Detection Using Convolution Neural Networks
Keywords:
Diabetic retinopathy, Deep Learning, CNN, SVM, Back PropagationAbstract
An image is defined as a scalar in case of performing a single measurement at every location of the image. As the invasive devices, having higher speed, and more accuracy are developed rapidly, a great innovation can be seen in the progress of medical imaging in last few decades. There is a necessity of precise software for handling these kinds of images of higher quality. The previous technique aims to pre-process the image for detecting the diabetes retinopathy (DR) and deploys the textural feature analysis to extract the pre-processed image. The diabetic portion is segmented using an optical disk segmentation (ODS) method. This research work expands the previous technique with the implementation of Convolutional Neural Network (CNN) algorithm. Google Colab is executed to simulate the suggested technique. Various parameters are utilized to analyze the results.
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L. Brunese, F. Mercaldo, A. Reginelli and A. Santone, "Neural Networks for Lung Cancer Detection through Radiomic Features," 201 R. S. Rajkumar, T. Jagathishkumar, D. Ragul and A. G. Selvarani, "Transfer Learning Approach for Diabetic Retinopathy Detection using Residual Network," 2021 6th International Conference on Inventive Computation Technologies (ICICT), 2021, pp. 1189-1193
L. Qiao, Y. Zhu and H. Zhou, "Diabetic Retinopathy Detection Using Prognosis of Microaneurysm and Early Diagnosis System for Non-Proliferative Diabetic Retinopathy Based on Deep Learning Algorithms," in IEEE Access, vol. 8, pp. 104292-104302, 2020
B. Shi et al., "GoogLeNet-based Diabetic-retinopathy-detection," 2022 14th International Conference on Advanced Computational Intelligence (ICACI), 2022, pp. 246-249
A. Reethika, J. Sathish, P. K. Priya, F. D. Shadrach and M. S. Kanivarshini, "Diabetic Retinopathy Detection Using Statistical Features," 2022 2nd International Conference on Innovative Practices in Technology and Management (ICIPTM), 2022, pp. 44-48
Z. A. Omar, M. Hanafi, S. Mashohor, N. F. M. Mahfudz, M. Muna’im, “Automatic Diabetic Retinopathy Detection and Classification System”, 2017 7th IEEE International Conference on System Engineering and Technology (ICSET 2017)
V. Mohana Guru Sai Gupta, Surya Teja D., Sanchit Gupta and Prateek Sengar, “Extraction of Blood Veins from the Fundus Image to Detect Diabetic Retinopathy”, 1st IEEE International Conference on Power Electronics, Intelligent Control and Energy Systems (ICPEICES-2016)
T. Young, Gerbrands, “Fundamentals of Image Processing”, Paper Back, ISBN 90-756, 9th January 2007.
Dutta MK, ParthaSarathi M, Ganguly S, Ganguly S, Srivastava K, “An efficient image processing-based technique for comprehensive detection and grading of non-proliferative diabetic retinopathy from fundus images”, 2017, Comput Methods Biomech Biomed Eng Imaging Vis 5(3):195–207
Khin Yadanar Win, Somsak Choomchuay, “Automated detection of exudates using histogram analysis for Digital Retinal Images”, IEEE Conference, 2016 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS), 24-27 Oct. 2016.
Jiri Gazarek, Jiri Jan, Radim Kolar, Jan Odstrcilik, “Retinal nerve fibre layer detection in fundus camera images compared to results from optical coherence tomography”, IEEE Conference, 2011 International Conference on Image Information Processing, 3-5 Nov. 2011.
Lassada Sukkaew, Bunyarit Uyyanonvara, Sarah Barman, “Automatic Extraction of the Structure of the Retinal Blood Vessel Network of Premature Infants”, J Med Assoc Thai Vol. 90 No. 9 2007
M.M. Fraza, S.A. Barmana, P. Remagninoa, A. Hoppea, A. Basitb, B. Uyyanonvarac, A.R. Rudnickad, C.G. Owend, “An Approach to Localize The Retinal Blood Vessels Using BitPlanes And Centerline Detection”, Comput. Methods Programs Biomed, 2011
Ashish Issac, Malay Kishore Dutta, Carlos M. Travieso, “Automatic computer vision-based detection and quantitative analysis of indicative parameters for grading of diabetic retinopathy”, 2018, Neural Computing and Applications
Narjes Karami, Hossein Rabbani, “A Dictionary Learning Based Method for Detection of Diabetic Retinopathy in Color Fundus Images”, 2017, 10th Iranian Conference on Machine Vision and Image Processing
Shailesh Kumar, Basant Kumar, “Diabetic Retinopathy Detection by Extracting Area and Number of Microaneurysm from Colour Fundus Image”, 2018 5th International Conference on Signal Processing and Integrated Networks (SPIN)
Enrique V. Carrera, Andres Gonzalez, Ricardo Carrera, “Automated detection of diabetic retinopathy using SVM”, 2017 IEEE XXIV International Conference on Electronics, Electrical Engineering and Computing (INTERCON)
Nikita Kashyap, Dr. Dharmendra Kumar Singh, “Color Histogram Based Image Retrieval Technique for Diabetic Retinopathy Detection”, 2017 2nd International Conference for Convergence in Technology (I2CT)
Harini R, Sheela N, “Feature Extraction and Classification of Retinal Images for Automated Detection of Diabetic Retinopathy”, 2016 Second International Conference on Cognitive Computing and Information Processing (CCIP)
Gardner, G., Keating, D., Williamson, T., Elliott, A. Automatic detection of diabetic retinopathy using an artificial neural network: a screening tool. Brit J Ophthalmol 1996;80(11):940–944.
Nayak, J., Bhat, P.S., Acharya, R., Lim, C., Kagathi, M. Automated identification of diabetic retinopathy stages using digital fundus images. J Med Syst 2008;32(2):107–115.
Acharya, R., Chua, C.K., Ng, E., Yu, W., Chee, C. Application of higher order spectra for the identification of diabetes retinopathy stages. J Med Syst 2008;32(6):481–488.
Acharya, U., Lim, C., Ng, E., Chee, C., Tamura, T. Computer-based detection of diabetes retinopathy stages using digital fundus images. P I Mech Eng H 2009;223(5):545–553.
Adarsh, P., Jeyakumari, D. Multiclass SVM-based automated diagnosis of diabetic retinopathy. In: Communications and Signal Processing (ICCSP), 2013 International Conference on. IEEE; 2013, p. 206–210.
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