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<citation_list><citation key="ref0"><doi>10.1080/21681163.2015.1051187</doi><unstructured_citation>Dutta MK, ParthaSarathi M, Ganguly S, Ganguly S, Srivastava K, &quot;An efficient image processing-based technique for comprehensive detection and grading of non-proliferative diabetic retinopathy from fundus images&quot;, 2017, Comput Methods Biomech Biomed Eng Imaging Vis 5(3):195-207</unstructured_citation></citation><citation key="ref1"><doi>10.1109/ICACCAF.2016.7749007</doi><unstructured_citation>V. Kumar, T. Lal, P. Dhuliya, and Diwaker Pant, &quot;A study and comparison of different image segmentation algorithms&quot;, In Advances in Computing, Communication, &amp; Automation (ICACCA)(Fall), International Conference on, IEEE 2016, pp. 1-6</unstructured_citation></citation><citation key="ref2"><doi>10.1109/WCCCT.2014.1</doi><unstructured_citation>R. Radha, and S. Jeyalakshmi, &quot;An effective algorithm for edges and veins detection in leaf images&quot;, In Computing and Communication Technologies (WCCCT), 2014 World Congress on, IEEE 2014, pp. 128-131</unstructured_citation></citation><citation key="ref3"><unstructured_citation>P. Gupta, &quot;A Survey Of Techniques And Applications For Real Time Image Processing&quot;, Journal of Global Research in Computer Science (UGC Approved Journal) 4, no. 8 (2013): 30-39</unstructured_citation></citation><citation key="ref4"><unstructured_citation>KhinYadanar Win, SomsakChoomchuay, &quot;Automated detection of exudates using histogram analysis for Digital Retinal Images&quot;, IEEE Conference, 2016 International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS), 24-27 Oct. 2016.</unstructured_citation></citation><citation key="ref5"><doi>10.1109/ICIIP.2011.6108883</doi><unstructured_citation>Jiri Gazarek, Jiri Jan, Radim Kolar, Jan Odstrcilik, &quot;Retinal nerve fibre layer detection in fundus camera images compared to results from optical coherence tomography&quot;, IEEE Conference, 2011 International Conference on Image Information Processing, 3-5 Nov. 2011.</unstructured_citation></citation><citation key="ref6"><unstructured_citation>M.M. Fraza, S.A. Barmana, P. Remagninoa, A. Hoppea, A. Basitb, B. Uyyanonvarac, A.R. Rudnickad, C.G. Owend, &quot;An Approach To Localize The Retinal Blood Vessels Using BitPlanes And Centerline Detection&quot;, Comput. Methods Programs Biomed, 2011</unstructured_citation></citation><citation key="ref7"><doi>10.1109/SPIN.2018.8474264</doi><unstructured_citation>Shailesh Kumar, Basant Kumar, &quot;Diabetic Retinopathy Detection by Extracting Area and Number of Microaneurysm from Colour Fundus Image&quot;, 2018, 5th International Conference on Signal Processing and Integv rated Networks (SPIN)</unstructured_citation></citation><citation key="ref8"><doi>10.1109/ISMSIT.2018.8567055</doi><unstructured_citation>ÖmerDeperlıoğlu,UtkuKöse, &quot;Diagnosis of Diabetic Retinopathy by Using Image Processing and Convolutional Neural Network&quot;,2018, 2nd International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT)</unstructured_citation></citation><citation key="ref9"><doi>10.1109/COMITCon.2019.8862217</doi><unstructured_citation>Mamta Arora, Mrinal Pandey, &quot;Deep Neural Network for Diabetic Retinopathy Detection&quot;, 2019 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COMITCon)</unstructured_citation></citation><citation key="ref10"><doi>10.1109/ACCESS.2020.2993937</doi><unstructured_citation>LifengQiao, Ying Zhu, Hui Zhou, &quot;Diabetic Retinopathy Detection Using Prognosis of Microaneurysm and Early Diagnosis System for Non-Proliferative Diabetic Retinopathy Based on Deep Learning Algorithms&quot;, 2020, IEEE Access</unstructured_citation></citation><citation key="ref11"><doi>10.1109/ICCUBEA.2018.8697387</doi><unstructured_citation>Sarika Ekatpure, Ruchi Jain, &quot;Red Lesion Detection in Digital Fundus Image Affected by Diabetic Retinopathy&quot;, 2018, Fourth International Conference on Computing Communication Control and Automation (ICCUBEA)</unstructured_citation></citation><citation key="ref12"><doi>10.1109/INCET49848.2020.9154021</doi><unstructured_citation>Trisha,Israj Ali, &quot;Intensity Based Optic Disk Detection for Automatic Diabetic Retinopathy&quot;, 2020, International Conference for Emerging Technology (INCET)</unstructured_citation></citation><citation key="ref13"><doi>10.1109/SIU.2018.8404369</doi><unstructured_citation>NurselYalçin, SeyfullahAlver, NeclaUluhatun, &quot;Classification of retinal images with deep learning for early detection of diabetic retinopathy disease&quot;, 2018, 26th Signal Processing and Communications Applications Conference (SIU)</unstructured_citation></citation><citation key="ref14"><doi>10.1109/ICCS45141.2019.9065446</doi><unstructured_citation>Aishwarya Singh Gautam, Saikat Kumar Jana, ManashPratim Dutta, &quot;Automated Diagnosis of Diabetic Retinopathy using image processing for non-invasive biomedical application&quot;, 2019, International Conference on Intelligent Computing and Control Systems (ICCS)</unstructured_citation></citation><citation key="ref15"><doi>10.1109/ICCCNT45670.2019.8944633</doi><unstructured_citation>NavoneelChakrabarty,Subhrasankar Chatterjee, &quot;An Offbeat Technique for Diabetic Retinopathy Detection using Computer Vision&quot;, 2019, 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT)</unstructured_citation></citation><citation key="ref16"><doi>10.1109/IranianMVIP.2017.8342333</doi><unstructured_citation>NarjesKarami, Hossein Rabbani, &quot;A dictionary learning based method for detection of diabetic retinopathy in color fundus images&quot;, 2017, 10th Iranian Conference on Machine Vision and Image Processing (MVIP)</unstructured_citation></citation><citation key="ref17"><doi>10.1109/ICTAI.2019.00056</doi><unstructured_citation>Qilei Chen, Xinzi Sun, Ning Zhang, Yu Cao,Benyuan Liu, &quot;Mini Lesions Detection on Diabetic Retinopathy Images via Large Scale CNN Features&quot;, 2019, IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI)</unstructured_citation></citation><citation key="ref18"><unstructured_citation>Xianglong Zeng, Haiquan Chen, Yuan Luo,Wenbin Ye, &quot;Automated Diabetic Retinopathy Detection Based on Binocular Siamese-Like Convolutional Neural Network&quot;, 2020, IEEE Access</unstructured_citation></citation><citation key="ref19"><doi>10.1109/ICSCAN.2019.8878786</doi><unstructured_citation>D. Palani, K. Venkatalakshmi, A. Reshma Jabeen, V. M. Arun Bharath Ram, &quot;Effective Detection of Diabetic Retinopathy From Human Retinal Fundus Images Using Modified FCM and IWPSO&quot;, 2019, IEEE International Conference on System, Computation, Automation and Networking (ICSCAN)</unstructured_citation></citation><citation key="ref20"><doi>10.1109/KI48306.2020.9039793</doi><unstructured_citation>SlavomírKajan, Jozef Goga, KristiánLacko,JarmilaPavlovičová, &quot;Detection of Diabetic Retinopathy Using Pretrained Deep Neural Networks&quot;, 2020, Cybernetics &amp; Informatics (K&amp;I)</unstructured_citation></citation><citation key="ref21"><doi>10.1109/IDEA49133.2020.9170712</doi><unstructured_citation>Vasima Khan, Deepshikha Patel, Tariq AzfarMeenai, Rajesh Shukla, &quot;Application of deep learning techniques for automating the detection of diabetic retinopathy in retinal fundus photographs&quot;, 2020, 2nd International Conference on Data, Engineering and Applications (IDEA)</unstructured_citation></citation><citation key="ref22"><doi>10.1109/ICACCS48705.2020.9074436</doi><unstructured_citation>M. Sugasri, V. Vibitha, M. Paveshkumar, SreeSanjanaa Bose S., &quot;Screening System for Early Detection of Diabetic Retinopathy&quot;, 2020, 6th International Conference on Advanced Computing and Communication Systems (ICACCS)</unstructured_citation></citation><citation key="ref23"><unstructured_citation>Cam-Hao Hua, Thien Huynh-The,Sungyoung Lee, &quot;DRAN: Densely Reversed Attention based Convolutional Network for Diabetic Retinopathy Detection&quot;, 2020, 42nd Annual International Conference of the IEEE Engineering in Medicine &amp; Biology Society (EMBC)</unstructured_citation></citation><citation key="ref24"><doi>10.1109/ISBI.2019.8759269</doi><unstructured_citation>Nabila Eladawi, Ahmed ElTanboly, Mohammed Elmogy, Mohammed Ghaza, LuayFraiwan, Ahmed Aboelfetouh, Alaa Riad, Robert Keynton, Magdi El-Azab, ShlomitSchaal, Ayman El-Baz, &quot;Diabetic Retinopathy Early Detection Based on OCT and OCTA Feature Fusion&quot; 2019, IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019)</unstructured_citation></citation><citation key="ref25"><doi>10.1109/ICOSEC49089.2020.9215270</doi><unstructured_citation>C. Jayakumari, Vidhya Lavanya, E P Sumesh, &quot;Automated Diabetic Retinopathy Detection and classification using ImageNet Convolution Neural Network using Fundus Images&quot;, 2020, International Conference on Smart Electronics and Communication (ICOSEC)</unstructured_citation></citation><citation key="ref26"><doi>10.1109/MACS48846.2019.9024812</doi><unstructured_citation>Tahira Nazir, Ali Javed, Momina Masood, Junaid Rashid, Samira Kanwal, &quot;Diabetic Retinopathy Detection based on Hybrid Feature Extraction and SVM&quot;, 2019, 13th International Conference on Mathematics, Actuarial Science, Computer Science and Statistics (MACS)</unstructured_citation></citation><citation key="ref27"><doi>10.1109/ACCESS.2020.3005044</doi><unstructured_citation>AsraMomeni Pour, HadiSeyedarabi, Seyed Hassan Abbasi Jahromi, Alireza Javadzadeh, &quot;Automatic Detection and Monitoring of Diabetic Retinopathy Using Efficient Convolutional Neural Networks and Contrast Limited Adaptive Histogram Equalization&quot;, 2020, IEEE Access</unstructured_citation></citation><citation key="ref28"><doi>10.1109/ICCSP.2018.8524234</doi><unstructured_citation>Kranthi Kumar Palavalasa, Bhavani Sambaturu, &quot;Automatic Diabetic Retinopathy Detection Using Digital Image Processing&quot;, 2018, International Conference on Communication and Signal Processing (ICCSP)</unstructured_citation></citation><citation key="ref29"><doi>10.1109/INTERCON.2017.8079692</doi><unstructured_citation>Enrique V. Carrera, Andrés González, Ricardo Carrera, &quot;Automated detection of diabetic retinopathy using SVM&quot;, 2017, IEEE XXIV International Conference on Electronics, Electrical Engineering and Computing (INTERCON)</unstructured_citation></citation><citation key="ref30"><unstructured_citation>https://gadsdeneye.com/wp-content/uploads/diabetic-retinopathy-vector.jpg</unstructured_citation></citation><citation key="ref31"><doi>10.5244/C.21.15</doi><unstructured_citation>T. Kauppi, V. Kalesnykiene, J.-K. Kamarainen, L. Lensu, I. Sorri, A. Raninen, R. Voutilainen, H. Uusitalo, H. Kälviäinen, J. Pietilä, &quot;Diaretdb1 diabetic retinopathy database and evaluation protocol&quot;, Proc. Medical Image Understanding and Analysis (MIUA), vol. 2007, 2007.</unstructured_citation></citation><citation key="ref32"><journal_title>Neural Computing and Applications</journal_title><author>Issac</author><cYear>2018</cYear><doi>10.1007/s00521-018-3443-z</doi><article_title>Automatic computer vision-based detection and quantitative analysis of indicative parameters for grading of diabetic retinopathy</article_title><unstructured_citation>Issac, A., Dutta, M. K., &amp; Travieso, C. M. (2018). Automatic computer vision-based detection and quantitative analysis of indicative parameters for grading of diabetic retinopathy. Neural Computing and Applications.</unstructured_citation></citation><citation key="ref33"><journal_title>Image Analysis &amp; Stereology</journal_title><author>Decencière</author><volume>33</volume><issue>3</issue><first_page>231</first_page><cYear>2014</cYear><doi>10.5566/ias.1155</doi><article_title>FEEDBACK ON A PUBLICLY DISTRIBUTED IMAGE DATABASE: THE MESSIDOR DATABASE</article_title><unstructured_citation>Decencière, E., Zhang, X., Cazuguel, G., Lay, B., Cochener, B., Trone, C., … Klein, J.-C. (2014). FEEDBACK ON A PUBLICLY DISTRIBUTED IMAGE DATABASE: THE MESSIDOR DATABASE. Image Analysis &amp; Stereology, 33(3), 231.</unstructured_citation></citation><citation key="ref34"><journal_title>IEEE Transactions on Medical Imaging</journal_title><author>Ricci</author><volume>26</volume><issue>10</issue><first_page>1357</first_page><cYear>2007</cYear><doi>10.1109/TMI.2007.898551</doi><article_title>Retinal Blood Vessel Segmentation Using Line Operators and Support Vector Classification</article_title><unstructured_citation>Ricci, E., &amp; Perfetti, R. (2007). Retinal Blood Vessel Segmentation Using Line Operators and Support Vector Classification. IEEE Transactions on Medical Imaging, 26(10), 1357-1365.</unstructured_citation></citation><citation key="ref35"><doi>10.18063/ieac.v1i1.770</doi><unstructured_citation>Sun J.,Du W., Shi N. (2018). A Survey of kNN Algorithm. Information Engineering and Applied Computing, 1-10.</unstructured_citation></citation></citation_list>
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