Hasirunnisa Shaik, Department of Computer Science & Engineering, VLITS, A.P, India
S. Deva Kumar , Department of Computer Science & Engineering, VFSTR, A.P, India
M. Gargi, Department of Computer Science & Engineering, VLITS, A.P, India
DS Bhupal Naik, Department of Computer Science & Engineering, VFSTR, A.P, India
Diabetic Retinopathy (DR) is one of the reasons for causing blindness globally. DR is a retinal disease that is found in diabetic patients. As performing retinal screening examinations is a time taken and unmet need on all diabetic patients, automated medical image analysis helps in identifying the disease severity. In this paper we study about the image classification using deep learning. The purpose of this study was to develop automated diagnostic technology for DR screening. In this paper we used AlexNet design with convolutional neural networks for DR classification. Here our algorithm processes all the fundus images and classified them as affected or not. A total of 800 images were taken from MESSIDOR dataset to train and test a model to separate non-affected from those which are affected. By using this algorithm our model get good results in Accuracy.
AlexNet, Diabetic Retinopathy, Convolutional Neural Networks, Image Classification
[1] International Diabetes Federation (IDF). IDF Diabetes Atlas 7th edition; 2015. http://www.diabetesatlas.org/. Accessed, October 20, 2016.
[2] Beagley J, Guariguata L, Weil C, Motala AA. Global estimates of undiagnosed diabetes in adults. Diabetes Res Clin Pract.2015;103:150-160.
[3] Wilkinson CP, Ferris FL, Klein RE, et al. Proposed international clinical diabetic retinopathy and diabetic macular edema disease severity scales. Ophthalmology. 2003;110:1677-1682.
[4] J. Jayashree, J. Vijayashree and N. Ch. S. N. Iyengar, "Risk Level Prediction of Diabetic Retinopathy Using Adaptive Neural Fuzzy Inference System", International Journal of Advanced Science and Technology, SERSC Australia, ISSN: 2005-4238 (Print); 2207-6360 (Online), vol. 120, November (2018), (pp. 59-72), http://dx.doi.org/10.14257/ijast.2018.120.05.
[5] Ozieh MN, Bishu KG, Dismuke CE, Egede LE. Trends in health care expenditure in U.S. adults with diabetes:2002e2011. Diabetes Care. 2015;38:1844-1851.
[6] Sellahewa L, Simpson C, Maharajan P, et al. Grader agreement, and sensitivity and specificity of digital photography in a community optometry-based diabetic eye screening program. Clin Ophthalmol. 2014;8:1345-1349.
[7] Ruamviboonsuk P, Wongcumchang N, Surawongsin P, et al. Screening for diabetic retinopathy in rural area using single-field, digital fundus images. J Med Assoc Thail. 2005;88:176-180.
[8] Debnath Bhattacharyya and N. Thirupathi Rao, "Detection of Cyst in Human Brain Using Scan Images", International Journal of Advanced Science and Technology, NADIA, ISSN: 2005-4238 (Print); 2207-6360 (Online), vol. 124, March (2019), (pp. 111-120), http://dx.doi.org/10.33832/ijast.2019.124.10.
[9] Sanjay Yadav and A. K. Thripati, "Comparative Analysis of Canny Edge based Image Steganography with RSA Encryption", International Journal of Advanced Science and Technology, SERSC Australia, ISSN: 2005-4238 (Print); 2207-6360 (Online), vol. 104, July (2017), pp. 43-52, http://dx.doi.org/10.14257/ijast.2017.104.05.
[10] DongYue Wang and TaegKeun Whangbo, "Automatic Diagnostic System for Parkinson’s Disease Based on Deep Learning using Midbrain Magnetic Resonance Images", International Journal of Advanced Science and Technology, NADIA, ISSN: 2005-4238 (Print); 2207-6360 (Online), vol. 124, March (2019), (pp. 1-20), http://dx.doi.org/10.33832/ijast.2019.124.01.
[11] Mohammad Shah Alamgir and Sumit Dev, "Design and Implementation of an Automatic Voltage Regulator with a Great Precision and Proper Hysteresis", International Journal of Advanced Science and Technology, SERSC Australia, ISSN: 2005-4238 (Print); 2207-6360 (Online), vol. 75, February (2015), pp. 21-32, http://dx.doi.org/10.14257/ijast.2015.75.03.
[12] LeCun Y, Kavukcuoglu K, Farabet C. Convolutional networks and applications in vision. ISCAS 2010d2010 IEEE International Symposium on Circuits and Systems. Nano-Bio Circuit Fabrics and Systems; 2010:253-256.
[13] Lecun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521:436-444.
[14] Kaggle, Inc. Diabetic retinopathy detection; 2015. https://www.kaggle.com/c/diabetic-retinopathy-detection.Accessed 1.12.2016.
[15] Cuadros J, Bresnick G, Affiliations A. EyePACS: an adaptable telemedicine system for diabetic retinopathy screening. J Diabetes Sci Technol. 2009;3:509-516.
[16] Gulshan, V.; Peng, L.; Coram, M.; Stumpe, M.C.;Wu, D.; Narayanaswamy, A.; Venugopalan, S.;Widner, K.; Madams, T.; Cuadros, J. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA 2016, 316, 2402–2410. [CrossRef] [PubMed] .
[17] Raju Anitha, S. Jyothi, Venkata Naresh Mandhala, Debnath Bhattacharyya and Tai-hoon Kim, "Deep Learning Image Processing Technique for Early Detection of Alzheimer's Disease", International Journal of Advanced Science and Technology, SERSC Australia, ISSN: 2005-4238 (Print); 2207-6360 (Online), vol. 107, October (2017), pp. 85-104, http://dx.doi.org/10.14257/ijast.2017.107.07.
[18] Nesreen Samer El-Jerjawi and Samy S. Abu-Naser, "Diabetes Prediction Using Artificial Neural Network", International Journal of Advanced Science and Technology, SERSC Australia, ISSN: 2005-4238 (Print); 2207-6360 (Online), vol. 121, December (2018), (pp. 55-64), http://dx.doi.org/10.14257/ijast.2018.121.05.
[19] Seoud, L.; Chelbi, J.; Cheriet, F. Automatic grading of diabetic retinopathy on a public database. In Proceedings of the Ophthalmic Medical Image Analysis Second International Workshop, Munich, Germany, 9 October 2015.
[20] https://in.mathworks.com/matlabcentral/fileexchange/59133-neural-network-toolbox-tm--model-for-alexnet-network.
[21] Suolan Liu, Chen Yue and Hongyuan Wang, "An Improved Hybrid Encryption Algorithm for RGB Images", International Journal of Advanced Science and Technology, SERSC Australia, ISSN: 2005-4238 (Print); 2207-6360 (Online), vol. 95, October (2016), pp. 37-44, http://dx.doi.org/10.14257/ijast.2016.95.04.
[22] Ankita Wadhawan and Avani Bhatia, "Neural Network Based Intelligent Retrieval System for Verifying Dynamic Signatures", International Journal of Advanced Science and Technology, SERSC Australia, ISSN: 2005-4238 (Print); 2207-6360 (Online), vol. 83, October (2015), pp. 27-40, http://dx.doi.org/10.14257/ijast.2015.83.03.
[23] Lyamine Briki and Kamel Djeghaba, "A New Plasticity Model for Concrete in Compression Based on Artificial Neural Networks", International Journal of Advanced Science and Technology, SERSC Australia, ISSN: 2005-4238 (Print); 2207-6360 (Online), vol. 75, February (2015), pp. 43-50, http://dx.doi.org/10.14257/ijast.2015.75.05.
[24] Deep Learning with MATLAB – matlab expo2018.
[25] Introducing Deep Learning with the MATLAB – Deep Learning E-Book provided by the mathworks.
APA:
Shaik, H., Kumar, S. D., Gargi, M., & Bhupal Naik, D. S. (2019). Automated Identification of Diabetic Retinopathy Using Alexnet Architecture. International Journal of Advanced Science and Technology (IJAST), ISSN: 2005-4238(Print); 2207-6360 (Online), NADIA, 132, 11-18. doi: 10.33832/ijast.2019.132.02.
MLA:
Shaik, Hasirunnisa, et al. “Automated Identification of Diabetic Retinopathy Using Alexnet Architecture.” International Journal of Advanced Science and Technology, ISSN: 2005-4238(Print); 2207-6360 (Online), NADIA, vol. 132, 2019, pp. 11-18. IJAST, http://article.nadiapub.com/IJAST/Vol132/2.html.
IEEE:
[1] H. Shaik, S. Deva Kumar, M. Gargi and D. S. Bhupal Naik, “Automated Identification of Diabetic Retinopathy Using Alexnet Architecture.” International Journal of Advanced Science and Technology (IJAST), ISSN: 2005-4238(Print); 2207-6360 (Online), NADIA, vol. 132, pp. 1-18, Nov 2019.
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