Classification of Diabetic Retinopathy types based on Convolution Neural Network (CNN)

Document Type : Original Article

Authors

1 Communications and Electronics Department Faculty of Electronic Engineering,Manoufia University: Menouf, Egypt

2 Communications and Electronics Department Faculty of Engineering, Minia University: Menouf, Egypt

3 Department of Robotics and intelligent machines, Faculty of artificial intelligents Kafrelsheikh University: Egypt

4 Communications and Electronics Department Faculty of Electronic Engineering,Manoufia University: Menouf, Egypt,

5 Electrical engineering Department Faculty of Engineering,Minia University: Egypt

6 Automatic Control Department Faculty of Electronic Engineering,Manoufia University: Menouf, Egypt

Abstract

- Diabetes mellitus have an eye disease called diabetic retinopathy. The early discovery of the disease is a great achievement in management of diabetic retinopathy. We use Fundus images are used for identification of the nature of an illness or other problem through examination of the symptoms to check for any abnormalities or any change in the retina. In this paper, Convolutional Neural Networks (CNN) is performed to classify the retinal fundus images to normal, background and pre-proliferative retinopathy. The proposed model consists of 5 convolutional layers followed by 5 max pooling layers. Finally, a global average pooling is used. In this work we achieve accuracy reached 95.23%.

Keywords

References

[1]     Statistics on Diabetes and Dabetic Retinopathy; The National Diabetes Information Clearinghouse (NDIC http://diabetes.niddk.nih.gov/dm/pubs/statistics/#Diagnosed20 (Access Date 25 may  2016).).
[2]     Cassin, B. and Solomon, S. Dictionary of Eye Terminology. Gainesville, Florida: Triad Publishing Company, 1990.
[3]     Amit B. Jain, Vadivelu Jaya Prakash and Muna Bhende. "Techniques of Fundus Imaging".J. Wang et al., A research on security and privacy issues for patient related data in medical organization system. Int. J. Secur. Appl., 287–298 (2013)
[4]     https://www.diabetes.co.uk/diabetes-complications/background-retinopathy.html.
[5]     https://oxfordmedicine.com/view/10.1093/med/9780199544967.001.0001/med-9780199544967-chapter-7.
[6]     M. D. Zeiler and R. Fergus. Visualizing and understanding convolutional networks. In ECCV, 2014.
[7]     Mookiah, M.R.K., Acharya, U.R., Chua, C.K., Lim, C.M., Ng, E., Laude, A.. Computer-aided diagnosis of diabetic retinopathy: Areview. Comput Biol Med 2013;43(12):2136–2155.
[8]     Gardner, G., Keating, D., Williamson, T., Elliott, A.. Automatic detection of diabetic retinopathy using an artificial neural network: ascreening tool. Brit J Ophthalmol 1996;80(11):940–944.
[9]     https://oxfordmedicine.com/view/10.1093/med/9780199544967.001.0001/med-9780199544967-chapter-7
[10]  M. D. Zeiler and R. Fergus. Visualizing and understanding convolutional networks. In ECCV, 2014.
[11]  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.
[12]  12.            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.
[13]  13.            Piotr ,S., Majumdar, F., Caliva, B., Al-Diri, A.. Microaneurysm Detection using Fully          Convolutional Neural Network. Computer Methods and Programs in Biomedicine (2018).
[14]  Noushin, H., Pourreza, M., Masoudi, K.,Ghiasi Shirazi , E. Microaneurysm detection in fundus images using a two step convolutional neural network  . BioMed Eng OnLine (2019(.
[15]  Oscar, J., Arevaloa, F., A. Gonaleza aMindlab, Universidad N., de Colombia, B..  Convolutional network to detect exudates in eye fundus images of diabetic subjects. 12th International Symposium on Medical Information Processing and Analysis; 101600T (2017).
[16]  Mark J. J. P. van Grinsven, B., Ginneken, C., B. Hoyng, T.. Fast convolutional neural network training using selective data sampling:Application to hemorrhage detection in color fundus image.IEEE Transactions on Medical Imaging.
[17]  Kele , D., Feng, H.. Deep Convolutional Neural Network-Based Early Automated Detection of Diabetic Retinopathy Using Fundus Image. Selected Papers from the Second CCF Bioinformatics Conference (CBC (2017)).
[18]  Harry,P.,Frans.,C. Convolutional Neural Networks for Diabetic Retinopathy. International Conference On Medical Imaging Understanding and Analysis 2016.
[19]    Gabriel, G., Jhair ,G.. Detection of diabetic retinopathy based on a convolutional neural network using retinal fundus images.
[20]  ARIA (Automatic Retinal Image Analysis) . Electronic material (Online). Available online at: https://github.com/petebankhead/ARIA.
[21]  Structured analysis of the retina (STARE). Electronic material (Online). Available online at: http://www.ces.clemson.edu/∼ahoover/stare/ [referred 16.12.2009.
[22]  Digital retinal images for vessel extraction (DRIVE). Electronic material (Online).Available online at: http://www.isi.uu.nl/Research/Databases/DRIVE/ [referred 16.12.2009].
.
 
[1]     Statistics on Diabetes and Dabetic Retinopathy; The National Diabetes Information Clearinghouse (NDIC http://diabetes.niddk.nih.gov/dm/pubs/statistics/#Diagnosed20 (Access Date 25 may  2016).).
[2]     Cassin, B. and Solomon, S. Dictionary of Eye Terminology. Gainesville, Florida: Triad Publishing Company, 1990.
[3]     Amit B. Jain, Vadivelu Jaya Prakash and Muna Bhende. "Techniques of Fundus Imaging".J. Wang et al., A research on security and privacy issues for patient related data in medical organization system. Int. J. Secur. Appl., 287–298 (2013)
[4]     https://www.diabetes.co.uk/diabetes-complications/background-retinopathy.html.
[5]     https://oxfordmedicine.com/view/10.1093/med/9780199544967.001.0001/med-9780199544967-chapter-7.
[6]     M. D. Zeiler and R. Fergus. Visualizing and understanding convolutional networks. In ECCV, 2014.
[7]     Mookiah, M.R.K., Acharya, U.R., Chua, C.K., Lim, C.M., Ng, E., Laude, A.. Computer-aided diagnosis of diabetic retinopathy: Areview. Comput Biol Med 2013;43(12):2136–2155.
[8]     Gardner, G., Keating, D., Williamson, T., Elliott, A.. Automatic detection of diabetic retinopathy using an artificial neural network: ascreening tool. Brit J Ophthalmol 1996;80(11):940–944.
[9]     https://oxfordmedicine.com/view/10.1093/med/9780199544967.001.0001/med-9780199544967-chapter-7
[10]  M. D. Zeiler and R. Fergus. Visualizing and understanding convolutional networks. In ECCV, 2014.
[11]  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.
[12]  12.            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.
[13]  13.            Piotr ,S., Majumdar, F., Caliva, B., Al-Diri, A.. Microaneurysm Detection using Fully          Convolutional Neural Network. Computer Methods and Programs in Biomedicine (2018).
[14]  Noushin, H., Pourreza, M., Masoudi, K.,Ghiasi Shirazi , E. Microaneurysm detection in fundus images using a two step convolutional neural network  . BioMed Eng OnLine (2019(.
[15]  Oscar, J., Arevaloa, F., A. Gonaleza aMindlab, Universidad N., de Colombia, B..  Convolutional network to detect exudates in eye fundus images of diabetic subjects. 12th International Symposium on Medical Information Processing and Analysis; 101600T (2017).
[16]  Mark J. J. P. van Grinsven, B., Ginneken, C., B. Hoyng, T.. Fast convolutional neural network training using selective data sampling:Application to hemorrhage detection in color fundus image.IEEE Transactions on Medical Imaging.
[17]  Kele , D., Feng, H.. Deep Convolutional Neural Network-Based Early Automated Detection of Diabetic Retinopathy Using Fundus Image. Selected Papers from the Second CCF Bioinformatics Conference (CBC (2017)).
[18]  Harry,P.,Frans.,C. Convolutional Neural Networks for Diabetic Retinopathy. International Conference On Medical Imaging Understanding and Analysis 2016.
[19]    Gabriel, G., Jhair ,G.. Detection of diabetic retinopathy based on a convolutional neural network using retinal fundus images.
[20]  ARIA (Automatic Retinal Image Analysis) . Electronic material (Online). Available online at: https://github.com/petebankhead/ARIA.
[21]  Structured analysis of the retina (STARE). Electronic material (Online). Available online at: http://www.ces.clemson.edu/∼ahoover/stare/ [referred 16.12.2009.
[22]  Digital retinal images for vessel extraction (DRIVE). Electronic material (Online).Available online at: http://www.isi.uu.nl/Research/Databases/DRIVE/ [referred 16.12.2009].
.
 
 
 
[1]     Statistics on Diabetes and Dabetic Retinopathy; The National Diabetes Information Clearinghouse (NDIC http://diabetes.niddk.nih.gov/dm/pubs/statistics/#Diagnosed20 (Access Date 25 may  2016).).
[2]     Cassin, B. and Solomon, S. Dictionary of Eye Terminology. Gainesville, Florida: Triad Publishing Company, 1990.
[3]     Amit B. Jain, Vadivelu Jaya Prakash and Muna Bhende. "Techniques of Fundus Imaging".J. Wang et al., A research on security and privacy issues for patient related data in medical organization system. Int. J. Secur. Appl., 287–298 (2013)
[4]     https://www.diabetes.co.uk/diabetes-complications/background-retinopathy.html.
[5]     https://oxfordmedicine.com/view/10.1093/med/9780199544967.001.0001/med-9780199544967-chapter-7.
[6]     M. D. Zeiler and R. Fergus. Visualizing and understanding convolutional networks. In ECCV, 2014.
[7]     Mookiah, M.R.K., Acharya, U.R., Chua, C.K., Lim, C.M., Ng, E., Laude, A.. Computer-aided diagnosis of diabetic retinopathy: Areview. Comput Biol Med 2013;43(12):2136–2155.
[8]     Gardner, G., Keating, D., Williamson, T., Elliott, A.. Automatic detection of diabetic retinopathy using an artificial neural network: ascreening tool. Brit J Ophthalmol 1996;80(11):940–944.
[9]     https://oxfordmedicine.com/view/10.1093/med/9780199544967.001.0001/med-9780199544967-chapter-7
[10]  M. D. Zeiler and R. Fergus. Visualizing and understanding convolutional networks. In ECCV, 2014.
[11]  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.
[12]  12.            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.
[13]  13.            Piotr ,S., Majumdar, F., Caliva, B., Al-Diri, A.. Microaneurysm Detection using Fully          Convolutional Neural Network. Computer Methods and Programs in Biomedicine (2018).
[14]  Noushin, H., Pourreza, M., Masoudi, K.,Ghiasi Shirazi , E. Microaneurysm detection in fundus images using a two step convolutional neural network  . BioMed Eng OnLine (2019(.
[15]  Oscar, J., Arevaloa, F., A. Gonaleza aMindlab, Universidad N., de Colombia, B..  Convolutional network to detect exudates in eye fundus images of diabetic subjects. 12th International Symposium on Medical Information Processing and Analysis; 101600T (2017).
[16]  Mark J. J. P. van Grinsven, B., Ginneken, C., B. Hoyng, T.. Fast convolutional neural network training using selective data sampling:Application to hemorrhage detection in color fundus image.IEEE Transactions on Medical Imaging.
[17]  Kele , D., Feng, H.. Deep Convolutional Neural Network-Based Early Automated Detection of Diabetic Retinopathy Using Fundus Image. Selected Papers from the Second CCF Bioinformatics Conference (CBC (2017)).
[18]  Harry,P.,Frans.,C. Convolutional Neural Networks for Diabetic Retinopathy. International Conference On Medical Imaging Understanding and Analysis 2016.
[19]    Gabriel, G., Jhair ,G.. Detection of diabetic retinopathy based on a convolutional neural network using retinal fundus images.
[20]  ARIA (Automatic Retinal Image Analysis) . Electronic material (Online). Available online at: https://github.com/petebankhead/ARIA.
[21]  Structured analysis of the retina (STARE). Electronic material (Online). Available online at: http://www.ces.clemson.edu/∼ahoover/stare/ [referred 16.12.2009.
[22]  Digital retinal images for vessel extraction (DRIVE). Electronic material (Online).Available online at: http://www.isi.uu.nl/Research/Databases/DRIVE/ [referred 16.12.2009].
.
 
 
 
[1]     Statistics on Diabetes and Dabetic Retinopathy; The National Diabetes Information Clearinghouse (NDIC http://diabetes.niddk.nih.gov/dm/pubs/statistics/#Diagnosed20 (Access Date 25 may  2016).).
[2]     Cassin, B. and Solomon, S. Dictionary of Eye Terminology. Gainesville, Florida: Triad Publishing Company, 1990.
[3]     Amit B. Jain, Vadivelu Jaya Prakash and Muna Bhende. "Techniques of Fundus Imaging".J. Wang et al., A research on security and privacy issues for patient related data in medical organization system. Int. J. Secur. Appl., 287–298 (2013)
[4]     https://www.diabetes.co.uk/diabetes-complications/background-retinopathy.html.
[5]     https://oxfordmedicine.com/view/10.1093/med/9780199544967.001.0001/med-9780199544967-chapter-7.
[6]     M. D. Zeiler and R. Fergus. Visualizing and understanding convolutional networks. In ECCV, 2014.
[7]     Mookiah, M.R.K., Acharya, U.R., Chua, C.K., Lim, C.M., Ng, E., Laude, A.. Computer-aided diagnosis of diabetic retinopathy: Areview. Comput Biol Med 2013;43(12):2136–2155.
[8]     Gardner, G., Keating, D., Williamson, T., Elliott, A.. Automatic detection of diabetic retinopathy using an artificial neural network: ascreening tool. Brit J Ophthalmol 1996;80(11):940–944.
[9]     https://oxfordmedicine.com/view/10.1093/med/9780199544967.001.0001/med-9780199544967-chapter-7
[10]  M. D. Zeiler and R. Fergus. Visualizing and understanding convolutional networks. In ECCV, 2014.
[11]  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.
[12]  12.            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.
[13]  13.            Piotr ,S., Majumdar, F., Caliva, B., Al-Diri, A.. Microaneurysm Detection using Fully          Convolutional Neural Network. Computer Methods and Programs in Biomedicine (2018).
[14]  Noushin, H., Pourreza, M., Masoudi, K.,Ghiasi Shirazi , E. Microaneurysm detection in fundus images using a two step convolutional neural network  . BioMed Eng OnLine (2019(.
[15]  Oscar, J., Arevaloa, F., A. Gonaleza aMindlab, Universidad N., de Colombia, B..  Convolutional network to detect exudates in eye fundus images of diabetic subjects. 12th International Symposium on Medical Information Processing and Analysis; 101600T (2017).
[16]  Mark J. J. P. van Grinsven, B., Ginneken, C., B. Hoyng, T.. Fast convolutional neural network training using selective data sampling:Application to hemorrhage detection in color fundus image.IEEE Transactions on Medical Imaging.
[17]  Kele , D., Feng, H.. Deep Convolutional Neural Network-Based Early Automated Detection of Diabetic Retinopathy Using Fundus Image. Selected Papers from the Second CCF Bioinformatics Conference (CBC (2017)).
[18]  Harry,P.,Frans.,C. Convolutional Neural Networks for Diabetic Retinopathy. International Conference On Medical Imaging Understanding and Analysis 2016.
[19]    Gabriel, G., Jhair ,G.. Detection of diabetic retinopathy based on a convolutional neural network using retinal fundus images.
[20]  ARIA (Automatic Retinal Image Analysis) . Electronic material (Online). Available online at: https://github.com/petebankhead/ARIA.
[21]  Structured analysis of the retina (STARE). Electronic material (Online). Available online at: http://www.ces.clemson.edu/∼ahoover/stare/ [referred 16.12.2009.
[22]  Digital retinal images for vessel extraction (DRIVE). Electronic material (Online).Available online at: http://www.isi.uu.nl/Research/Databases/DRIVE/ [referred 16.12.2009].
.
 
 
 
[1]     Statistics on Diabetes and Dabetic Retinopathy; The National Diabetes Information Clearinghouse (NDIC http://diabetes.niddk.nih.gov/dm/pubs/statistics/#Diagnosed20 (Access Date 25 may  2016).).
[2]     Cassin, B. and Solomon, S. Dictionary of Eye Terminology. Gainesville, Florida: Triad Publishing Company, 1990.
[3]     Amit B. Jain, Vadivelu Jaya Prakash and Muna Bhende. "Techniques of Fundus Imaging".J. Wang et al., A research on security and privacy issues for patient related data in medical organization system. Int. J. Secur. Appl., 287–298 (2013)
[4]     https://www.diabetes.co.uk/diabetes-complications/background-retinopathy.html.
[5]     https://oxfordmedicine.com/view/10.1093/med/9780199544967.001.0001/med-9780199544967-chapter-7.
[6]     M. D. Zeiler and R. Fergus. Visualizing and understanding convolutional networks. In ECCV, 2014.
[7]     Mookiah, M.R.K., Acharya, U.R., Chua, C.K., Lim, C.M., Ng, E., Laude, A.. Computer-aided diagnosis of diabetic retinopathy: Areview. Comput Biol Med 2013;43(12):2136–2155.
[8]     Gardner, G., Keating, D., Williamson, T., Elliott, A.. Automatic detection of diabetic retinopathy using an artificial neural network: ascreening tool. Brit J Ophthalmol 1996;80(11):940–944.
[9]     https://oxfordmedicine.com/view/10.1093/med/9780199544967.001.0001/med-9780199544967-chapter-7
[10]  M. D. Zeiler and R. Fergus. Visualizing and understanding convolutional networks. In ECCV, 2014.
[11]  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.
[12]  12.            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.
[13]  13.            Piotr ,S., Majumdar, F., Caliva, B., Al-Diri, A.. Microaneurysm Detection using Fully          Convolutional Neural Network. Computer Methods and Programs in Biomedicine (2018).
[14]  Noushin, H., Pourreza, M., Masoudi, K.,Ghiasi Shirazi , E. Microaneurysm detection in fundus images using a two step convolutional neural network  . BioMed Eng OnLine (2019(.
[15]  Oscar, J., Arevaloa, F., A. Gonaleza aMindlab, Universidad N., de Colombia, B..  Convolutional network to detect exudates in eye fundus images of diabetic subjects. 12th International Symposium on Medical Information Processing and Analysis; 101600T (2017).
[16]  Mark J. J. P. van Grinsven, B., Ginneken, C., B. Hoyng, T.. Fast convolutional neural network training using selective data sampling:Application to hemorrhage detection in color fundus image.IEEE Transactions on Medical Imaging.
[17]  Kele , D., Feng, H.. Deep Convolutional Neural Network-Based Early Automated Detection of Diabetic Retinopathy Using Fundus Image. Selected Papers from the Second CCF Bioinformatics Conference (CBC (2017)).
[18]  Harry,P.,Frans.,C. Convolutional Neural Networks for Diabetic Retinopathy. International Conference On Medical Imaging Understanding and Analysis 2016.
[19]    Gabriel, G., Jhair ,G.. Detection of diabetic retinopathy based on a convolutional neural network using retinal fundus images.
[20]  ARIA (Automatic Retinal Image Analysis) . Electronic material (Online). Available online at: https://github.com/petebankhead/ARIA.
[21]  Structured analysis of the retina (STARE). Electronic material (Online). Available online at: http://www.ces.clemson.edu/∼ahoover/stare/ [referred 16.12.2009.
[22]  Digital retinal images for vessel extraction (DRIVE). Electronic material (Online).Available online at: http://www.isi.uu.nl/Research/Databases/DRIVE/ [referred 16.12.2009].
.
 
[1]     Statistics on Diabetes and Dabetic Retinopathy; The National Diabetes Information Clearinghouse (NDIC http://diabetes.niddk.nih.gov/dm/pubs/statistics/#Diagnosed20 (Access Date 25 may  2016).).
[2]     Cassin, B. and Solomon, S. Dictionary of Eye Terminology. Gainesville, Florida: Triad Publishing Company, 1990.
[3]     Amit B. Jain, Vadivelu Jaya Prakash and Muna Bhende. "Techniques of Fundus Imaging".J. Wang et al., A research on security and privacy issues for patient related data in medical organization system. Int. J. Secur. Appl., 287–298 (2013)
[4]     https://www.diabetes.co.uk/diabetes-complications/background-retinopathy.html.
[5]     https://oxfordmedicine.com/view/10.1093/med/9780199544967.001.0001/med-9780199544967-chapter-7.
[6]     M. D. Zeiler and R. Fergus. Visualizing and understanding convolutional networks. In ECCV, 2014.
[7]     Mookiah, M.R.K., Acharya, U.R., Chua, C.K., Lim, C.M., Ng, E., Laude, A.. Computer-aided diagnosis of diabetic retinopathy: Areview. Comput Biol Med 2013;43(12):2136–2155.
[8]     Gardner, G., Keating, D., Williamson, T., Elliott, A.. Automatic detection of diabetic retinopathy using an artificial neural network: ascreening tool. Brit J Ophthalmol 1996;80(11):940–944.
[9]     https://oxfordmedicine.com/view/10.1093/med/9780199544967.001.0001/med-9780199544967-chapter-7
[10]  M. D. Zeiler and R. Fergus. Visualizing and understanding convolutional networks. In ECCV, 2014.
[11]  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.
[12]  12.            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.
[13]  13.            Piotr ,S., Majumdar, F., Caliva, B., Al-Diri, A.. Microaneurysm Detection using Fully          Convolutional Neural Network. Computer Methods and Programs in Biomedicine (2018).
[14]  Noushin, H., Pourreza, M., Masoudi, K.,Ghiasi Shirazi , E. Microaneurysm detection in fundus images using a two step convolutional neural network  . BioMed Eng OnLine (2019(.
[15]  Oscar, J., Arevaloa, F., A. Gonaleza aMindlab, Universidad N., de Colombia, B..  Convolutional network to detect exudates in eye fundus images of diabetic subjects. 12th International Symposium on Medical Information Processing and Analysis; 101600T (2017).
[16]  Mark J. J. P. van Grinsven, B., Ginneken, C., B. Hoyng, T.. Fast convolutional neural network training using selective data sampling:Application to hemorrhage detection in color fundus image.IEEE Transactions on Medical Imaging.
[17]  Kele , D., Feng, H.. Deep Convolutional Neural Network-Based Early Automated Detection of Diabetic Retinopathy Using Fundus Image. Selected Papers from the Second CCF Bioinformatics Conference (CBC (2017)).
[18]  Harry,P.,Frans.,C. Convolutional Neural Networks for Diabetic Retinopathy. International Conference On Medical Imaging Understanding and Analysis 2016.
[19]    Gabriel, G., Jhair ,G.. Detection of diabetic retinopathy based on a convolutional neural network using retinal fundus images.
[20]  ARIA (Automatic Retinal Image Analysis) . Electronic material (Online). Available online at: https://github.com/petebankhead/ARIA.
[21]  Structured analysis of the retina (STARE). Electronic material (Online). Available online at: http://www.ces.clemson.edu/∼ahoover/stare/ [referred 16.12.2009.
[22]  Digital retinal images for vessel extraction (DRIVE). Electronic material (Online).Available online at: http://www.isi.uu.nl/Research/Databases/DRIVE/ [referred 16.12.2009].
.
 
 
 
 
 
 
 
Menoufia Journal of Electronic Engineering Research
Volume 28, ICEEM2019-Special Issue
ICEEM2019-Special Issue: 1st International Conference on Electronic Eng., Faculty of Electronic Eng., Menouf, Egypt, 7-8 Dec.
2019
Pages 126-153

Files

Share

How to cite

Statistics