{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T23:24:46Z","timestamp":1780356286674,"version":"3.54.1"},"publisher-location":"Cham","reference-count":78,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030504014","type":"print"},{"value":"9783030504021","type":"electronic"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020]]},"DOI":"10.1007\/978-3-030-50402-1_9","type":"book-chapter","created":{"date-parts":[[2020,6,23]],"date-time":"2020-06-23T23:04:44Z","timestamp":1592953484000},"page":"136-154","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["Image Processing and Machine Learning Techniques for Diabetic Retinopathy Detection: A Review"],"prefix":"10.1007","author":[{"given":"Sarni Suhaila","family":"Rahim","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vasile","family":"Palade","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andreas","family":"Holzinger","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2020,6,24]]},"reference":[{"key":"9_CR1","unstructured":"International Diabetes Federation: IDF Diabetes Atlas, 8th edn. IDF, Belgium (2017)"},{"issue":"11","key":"9_CR2","doi-asserted-by":"crossref","first-page":"e442","DOI":"10.1371\/journal.pmed.0030442","volume":"3","author":"CD Mathers","year":"2006","unstructured":"Mathers, C.D., Loncar, D.: Projections of global mortality and burden of disease from 2002 to 2030. PLos Med. 3(11), e442 (2006)","journal-title":"PLos Med."},{"key":"9_CR3","unstructured":"World Health Organization: Global data on visual impairments 2010. WHO, Geneva (2012)"},{"key":"9_CR4","unstructured":"World Health Organization. \nhttp:\/\/www.who.int\/en\/news-room\/fact-sheets\/detail\/blindness-and-visual-impairment\n\n. Accessed 20 Mar 2019"},{"issue":"4","key":"9_CR5","doi-asserted-by":"crossref","first-page":"347","DOI":"10.1016\/j.survophthal.2012.01.004","volume":"57","author":"S Sivaprasad","year":"2012","unstructured":"Sivaprasad, S., Gupta, B., Crossby-Nwaobi, R., Evans, J.: Prevalence of diabetic retinopathy in various ethnic groups: a worldwide perspective. Surv. Ophthalmol. 57(4), 347\u2013370 (2012)","journal-title":"Surv. Ophthalmol."},{"key":"9_CR6","volume-title":"Current Management of Diabetic Retinopathy","author":"D Learned","year":"2018","unstructured":"Learned, D., Pieramici, D.: Epidemiology and natural history of diabetic retinopathy. In: Baumal, C.R. (ed.) Current Management of Diabetic Retinopathy. Elsevier, St. Louis (2018)"},{"key":"9_CR7","unstructured":"Health Technology Assessment Unit, Medical Development Division, Ministry of Health Malaysia: Report screening for diabetic retinopathy. Ministry of Health Malaysia, Kuala Lumpur (2002)"},{"key":"9_CR8","unstructured":"Ministry of Health Diabetic Retinopathy Screening Team: Handbook guide to diabetic retinopathy screening -Module 5-2012. Ministry of Health Malaysia, Putrajaya (2012)"},{"key":"9_CR9","doi-asserted-by":"crossref","DOI":"10.1002\/9781119968573","volume-title":"Handbook of Retinal Screening in Diabetes: Diagnosis and Management","author":"R Taylor","year":"2012","unstructured":"Taylor, R., Batey, D.: Handbook of Retinal Screening in Diabetes: Diagnosis and Management. Wiley, Chichester (2012)"},{"key":"9_CR10","unstructured":"American Academy of Ophthalmology Retina Panel: Preferred practice pattern guidelines. Diabetic retinopathy. American Academy of Ophthalmology, San Francisco (2008)"},{"key":"9_CR11","doi-asserted-by":"crossref","DOI":"10.1002\/9781444308174","volume-title":"A Practical Manual of Diabetic Retinopathy Management","author":"PH Scanlon","year":"2009","unstructured":"Scanlon, P.H., Wilkinson, C.P., Aldington, S.J., Matthews, D.R.: A Practical Manual of Diabetic Retinopathy Management. Wiley-Blackwell, Chicester (2009)"},{"key":"9_CR12","volume-title":"DIARETDB0: Evaluation Database and Methodology for Diabetic Retinopathy Algorithms","author":"T Kauppi","year":"2006","unstructured":"Kauppi, T., et al.: DIARETDB0: Evaluation Database and Methodology for Diabetic Retinopathy Algorithms. Lappeenranta University of Technology, Finland (2006)"},{"key":"9_CR13","volume-title":"DIARETDB1 Diabetic Retinopathy Database and Evaluation Protocol","author":"T Kauppi","year":"2007","unstructured":"Kauppi, T., et al.: DIARETDB1 Diabetic Retinopathy Database and Evaluation Protocol. Lappeenranta University of Technology, Finland (2007)"},{"key":"9_CR14","unstructured":"Messidor. \nhttp:\/\/www.adcis.net\/en\/third-party\/messidor\/\n\n. Accessed 28 Jan 2019"},{"key":"9_CR15","doi-asserted-by":"crossref","first-page":"501","DOI":"10.1109\/TMI.2004.825627","volume":"23","author":"JJ Staal","year":"2004","unstructured":"Staal, J.J., Abramoff, M.D., Niemeijer, M., Viergever, M.A., Ginneken-van, B.: Ridge based vessel segmentation in color images of the retina. IEEE Trans. Med. Imaging 23, 501\u2013509 (2004)","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"8","key":"9_CR16","doi-asserted-by":"crossref","first-page":"951","DOI":"10.1109\/TMI.2003.815900","volume":"22","author":"A Hoover","year":"2003","unstructured":"Hoover, A., Goldbaum, M.: Locating the optic nerve in a retinal image using the fuzzy convergence of the blood vessels. IEEE Trans. Med. Imaging 22(8), 951\u2013958 (2003)","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"3","key":"9_CR17","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1109\/42.845178","volume":"19","author":"A Hoover","year":"2000","unstructured":"Hoover, A., Kouznetsova, V., Goldbaum, M.: Locating blood vessels in retinal images by piece-wise threshold probing of a matched filter response. IEEE Trans. Med. Imaging 19(3), 203\u2013210 (2000)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"9_CR18","doi-asserted-by":"crossref","unstructured":"Al-Diri, B., Hunter, A., Steel, D., Habib, M., Hudaib, T., Berry, S.: REVIEW-A reference data set for retinal vessel profiles. In: Proceedings of the 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 2262\u20132265. IEEE, USA (2008)","DOI":"10.1109\/IEMBS.2008.4649647"},{"issue":"1","key":"9_CR19","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1109\/TMI.2009.2033909","volume":"29","author":"M Niemeijer","year":"2010","unstructured":"Niemeijer, M., et al.: Retinopathy online challenge: automatic detection of microaneurysms in digital color fundus photographs. IEEE Trans. Med. Imaging 29(1), 185\u2013195 (2010)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"9_CR20","unstructured":"Diabetic Retinopathy Detection. \nhttps:\/\/www.kaggle.com\/c\/diabetic-retinopathy-detection\n\n. Accessed 28 Jan 2019"},{"issue":"4","key":"9_CR21","doi-asserted-by":"publisher","first-page":"249","DOI":"10.1007\/s40708-016-0045-3","volume":"3","author":"SS Rahim","year":"2016","unstructured":"Rahim, S.S., Palade, V., Shuttleworth, J., Jayne, C.: Automatic screening and classification of diabetic retinopathy and maculopathy using fuzzy image processing. Brain Inform. 3(4), 249\u2013267 (2016). \nhttps:\/\/doi.org\/10.1007\/s40708-016-0045-3","journal-title":"Brain Inform."},{"key":"9_CR22","unstructured":"Indian Diabetic Retinopathy Image Dataset. \nhttps:\/\/idrid.grand-challenge.org\/Home\/\n\n. Accessed 28 Jan 2019"},{"key":"9_CR23","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/j.knosys.2012.09.008","volume":"39","author":"MRK Mookiah","year":"2013","unstructured":"Mookiah, M.R.K., et al.: Evolutionary algorithm based classifier parameter tuning for automatic diabetic retinopathy grading: a hybrid feature extraction approach. Knowl.-Based Syst. 39, 9\u201322 (2013)","journal-title":"Knowl.-Based Syst."},{"issue":"4","key":"9_CR24","first-page":"844","volume":"1","author":"R Priya","year":"2011","unstructured":"Priya, R., Aruna, P.: Review of automated diagnosis of diabetic retinopathy using the support vector machine. Int. J. Appl. Eng. Res. 1(4), 844\u2013863 (2011)","journal-title":"Int. J. Appl. Eng. Res."},{"issue":"1","key":"9_CR25","first-page":"6","volume":"41","author":"R Priya","year":"2012","unstructured":"Priya, R., Aruna, P.: SVM and neural network based diagnosis of diabetic retinopathy. Int. J. Comput. Appl. 41(1), 6\u201312 (2012)","journal-title":"Int. J. Comput. Appl."},{"key":"9_CR26","first-page":"34","volume":"1","author":"R Priya","year":"2013","unstructured":"Priya, R., Aruna, P., Suriya, R.: Image analysis technique for detecting diabetic retinopathy. Int. J. Comput. Appl. 1, 34\u201338 (2013)","journal-title":"Int. J. Comput. Appl."},{"issue":"6","key":"9_CR27","first-page":"2694","volume":"2","author":"SK Shome","year":"2011","unstructured":"Shome, S.K., Vadali, S.R.K.: Enhancement of diabetic retinopathy imagery using contrast limited adaptive histogram equalization. Int. J. Comput. Sci. Inf. Technol. 2(6), 2694\u20132699 (2011)","journal-title":"Int. J. Comput. Sci. Inf. Technol."},{"key":"9_CR28","unstructured":"Lam, C., Yi, D., Guo, M., Lindsey, T.: Automated detection of diabetic retinopathy using deep learning. In: AMIA Joint Summits on Translational Science Proceedings. AMIA Joint Summits on Translational Science\u00a02017, pp. 147\u2013155 (2018)"},{"key":"9_CR29","unstructured":"Voets, M., Mollersen, K., Bongo, L.A.: Replication study: development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. \nhttps:\/\/arxiv.org\/pdf\/1803.04337.pdf\n\n (2018)"},{"issue":"12","key":"9_CR30","first-page":"1","volume":"22","author":"K Xu","year":"2017","unstructured":"Xu, K., Feng, D., Mi, H.: Deep convolutional neural network-based early automated detection of diabetic retinopathy using fundus image. Molecules 22(12), 1\u20137 (2017)","journal-title":"Molecules"},{"key":"9_CR31","unstructured":"Rakhlin, A.: Diabetic retinopathy detection through integration of deep learning classification framework (2017). \nhttps:\/\/www.biorxiv.org\/content\/biorxiv\/early\/2018\/06\/19\/225508.full.pdf"},{"issue":"22","key":"9_CR32","doi-asserted-by":"publisher","first-page":"2402","DOI":"10.1001\/jama.2016.17216","volume":"316","author":"V Gulshan","year":"2016","unstructured":"Gulshan, V., Peng, L., Coram, M., et al.: Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA 316(22), 2402\u20132410 (2016). https:\/\/doi.org\/10.1001\/jama.2016.17216","journal-title":"JAMA"},{"key":"9_CR33","unstructured":"Rajanna, A.R., Aryafar, K., Ramchandran, R., Sisson, C., Shokoufandeh, A., Ptucha, R.: Neural networks with manifold learning for diabetic retinopathy detection. In: Proceedings of IEEE Western NY Image and Signal Processing Workshop. \nhttps:\/\/arxiv.org\/pdf\/1612.03961.pdf\n\n (2016)"},{"key":"9_CR34","doi-asserted-by":"crossref","first-page":"200","DOI":"10.1016\/j.procs.2016.07.014","volume":"90","author":"H Pratt","year":"2016","unstructured":"Pratt, H., Coenen, F., Broadbent, D.M., Harding, S.P., Zheng, Y.: Convolutional neural networks for diabetic retinopathy. Procedia Comput. Sci. 90, 200\u2013205 (2016)","journal-title":"Procedia Comput. Sci."},{"key":"9_CR35","doi-asserted-by":"crossref","unstructured":"Ghosh, R., Ghosh, K., Maitra, S.: Automatic detection and classification of diabetic retinopathy stages using CNN. In: 4th International Conference on Signal Processing and Integrated Networks (SPIN), pp. 550\u2013554. IEEE, USA (2017)","DOI":"10.1109\/SPIN.2017.8050011"},{"key":"9_CR36","doi-asserted-by":"crossref","unstructured":"Adal, K.M., Ali, S., Sidibe, D., Karnowski, T., Chaum, E., Meriaudeau, F.: Automated detection of microaneurysms using robust blob descriptors. In: SPIE Medical Imaging-Computer Aided Diagnosis, vol. 8670, no. 22 (2013)","DOI":"10.1117\/12.2007913"},{"key":"9_CR37","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.cmpb.2013.12.009","volume":"114","author":"KM Adal","year":"2014","unstructured":"Adal, K.M., Sidibe, D., Ali, S., Chaum, E., Karnowski, T.P., Meriaudeau, F.: Automated detection of microaneurysms using scale-adapted blob analysis and semi-supervised learning. Comput. Methods Programs Biomed. 114, 1\u201310 (2014)","journal-title":"Comput. Methods Programs Biomed."},{"key":"9_CR38","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1016\/j.compbiomed.2013.11.014","volume":"45","author":"MU Akram","year":"2014","unstructured":"Akram, M.U., Khalid, S., Tariq, A., Khan, S.A., Azam, F.: Detection and classification of retinal lesions for grading of diabetic retinopathy. Comput. Biol. Med. 45, 161\u2013171 (2014)","journal-title":"Comput. Biol. Med."},{"key":"9_CR39","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1016\/j.patcog.2012.07.002","volume":"46","author":"MU Akram","year":"2012","unstructured":"Akram, M.U., Khalid, S., Khan, S.A.: Identification and classification of microaneurysms for early detection of diabetic retinopathy. Pattern Recogn. 46, 107\u2013116 (2012)","journal-title":"Pattern Recogn."},{"key":"9_CR40","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2012\/761901","volume":"2012","author":"SHM Alipour","year":"2012","unstructured":"Alipour, S.H.M., Rabbani, H., Akhlaghi, M.R.: Diabetic retinopathy grading by digital curvelet transform. Comput. Math. Med. 2012, 1\u201311 (2012)","journal-title":"Comput. Math. Med."},{"key":"9_CR41","doi-asserted-by":"crossref","first-page":"403","DOI":"10.1016\/j.compmedimag.2013.05.001","volume":"37","author":"B Antal","year":"2013","unstructured":"Antal, B., Hajdu, A.: Improving microaneurysm detection in color fundus images by using context-aware approaches. Comput. Med. Imaging Graph. 37, 403\u2013408 (2013)","journal-title":"Comput. Med. Imaging Graph."},{"key":"9_CR42","unstructured":"Aravind, C., Ponnibala, M., Vijayachitra, S.: Automatic detection of microaneurysms and classification of diabetic retinopathy images using SVM technique. In: IJCA Proceedings on International Conference on Innovations in Intelligent Instrumentation, Optimization and Electrical Sciences ICIIIOES, no. 11, pp. 18\u201322 (2013)"},{"key":"9_CR43","doi-asserted-by":"crossref","unstructured":"Hatanaka, Y., Inoue, T., Okumura, S., Muramatsu, C., Fujita, H.: Automated microaneurysm detection method based on double-ring filter and feature analysis in retinal fundus images. In: Soda, P. (eds.) Proceedings of the 25th International Symposium on Computer-Based Medical Systems, CBMS, pp. 1\u20134. IEEE, USA (2012)","DOI":"10.1109\/CBMS.2012.6266339"},{"key":"9_CR44","doi-asserted-by":"crossref","first-page":"274","DOI":"10.1016\/j.cmpb.2011.06.007","volume":"107","author":"C Kose","year":"2012","unstructured":"Kose, C., Sevik, U., Ikibas, C., Erdol, H.: Simple methods for segmentation and measurement of diabetic retinopathy lesions in retinal fundus images. Comput. Methods Programs in Biomed. 107, 274\u2013293 (2012)","journal-title":"Comput. Methods Programs in Biomed."},{"issue":"9","key":"9_CR45","first-page":"1727","volume":"2","author":"RV Lichode","year":"2013","unstructured":"Lichode, R.V., Kulkarni, P.S.: Automatic diagnosis of diabetic retinopathy by hybrid multilayer feed forward neural network. Int. J. Sci. Eng. Technol. Res. (IJSETR) 2(9), 1727\u20131733 (2013)","journal-title":"Int. J. Sci. Eng. Technol. Res. (IJSETR)"},{"key":"9_CR46","first-page":"31","volume":"4","author":"J Prakash","year":"2013","unstructured":"Prakash, J., Sumanthi, K.: Detection and classification of microaneurysms for diabetic retinopathy. Int. J. Eng. Res. Appl. 4, 31\u201336 (2013)","journal-title":"Int. J. Eng. Res. Appl."},{"issue":"4","key":"9_CR47","first-page":"563","volume":"3","author":"R Priya","year":"2013","unstructured":"Priya, R., Aruna, P.: Diagnosis of diabetic retinopathy using machine learning techniques. J. Soft Comput. 3(4), 563\u2013575 (2013)","journal-title":"J. Soft Comput."},{"key":"9_CR48","doi-asserted-by":"crossref","unstructured":"Punnolil, A.: A novel approach for diagnosis and severity grading of diabetic maculopathy. In: Proceedings of the 2013 International Conference on Advances in Computing, Communications and Informatics, pp. 1230\u20131235. IEEE, New York (2013)","DOI":"10.1109\/ICACCI.2013.6637353"},{"key":"9_CR49","doi-asserted-by":"crossref","first-page":"186","DOI":"10.1016\/j.cmpb.2012.03.004","volume":"108","author":"MD Saleh","year":"2012","unstructured":"Saleh, M.D., Eswaran, C.: An automated decision-support system for non-proliferative diabetic retinopathy disease based on Mas and HAs detection. Comput. Methods Programs Biomed. 108, 186\u2013196 (2012)","journal-title":"Comput. Methods Programs Biomed."},{"issue":"11","key":"9_CR50","first-page":"762","volume":"2","author":"D Selvathi","year":"2012","unstructured":"Selvathi, D., Prakash, N.B., Balagopal, N.: Automated detection of diabetic retinopathy for early diagnosis using feature extraction and support vector machine. Int. J. Emerg. Technol. Adv. Eng. 2(11), 762\u2013767 (2012)","journal-title":"Int. J. Emerg. Technol. Adv. Eng."},{"issue":"2","key":"9_CR51","first-page":"294","volume":"7","author":"A Sopharak","year":"2013","unstructured":"Sopharak, A., Uyyanonvara, B., Barman, S.: Automated microaneurysm detection algorithms applied to diabetic retinopathy retinal images. Maejo Int. J. Sci. Technol. 7(2), 294\u2013314 (2013)","journal-title":"Maejo Int. J. Sci. Technol."},{"issue":"19","key":"9_CR52","first-page":"26","volume":"47","author":"SB Sujithkumar","year":"2012","unstructured":"Sujithkumar, S.B., Vipula, S.: Automatic detection of diabetic retinopathy in non-dilated RGB retinal fundus images. Int. J. Comput. Appl. 47(19), 26\u201332 (2012)","journal-title":"Int. J. Comput. Appl."},{"issue":"1","key":"9_CR53","first-page":"100","volume":"2","author":"C Sundhar","year":"2014","unstructured":"Sundhar, C., Archana, D.: Automatic screening of fundus images for detection of diabetic retinopathy. Int. J. Commun. Comput. Technol. 2(1), 100\u2013105 (2014)","journal-title":"Int. J. Commun. Comput. Technol."},{"key":"9_CR54","doi-asserted-by":"crossref","unstructured":"Chudzik, P., Majumdar, S., Caliva, F., Al-Diri, B., Hunter, A.: Microaneurysm detection using deep learning and interleaved freezing. In: Proceedings SPIE 10574, Medical Imaging 2018: Image Processing 1057411, pp. 1\u20139 (2018)","DOI":"10.1117\/12.2293520"},{"issue":"1","key":"9_CR55","doi-asserted-by":"crossref","first-page":"590","DOI":"10.1167\/iovs.17-22721","volume":"59","author":"C Lam","year":"2018","unstructured":"Lam, C., Yu, C., Huang, L., Rubin, D.: Retinal lesion detection with deep learning using image patches. Invest. Ophthalmol. Vis. Sci. 59(1), 590\u2013596 (2018)","journal-title":"Invest. Ophthalmol. Vis. Sci."},{"key":"9_CR56","doi-asserted-by":"crossref","unstructured":"Hatanaka, Y., Ogohara, K., Sunayama, W., Miyashita, M., Muramatsu, C., Fujita, H.: Automatic microaneurysms detection on retinal images using deep convolution neural network. In: International Workshop on Advanced Image Technology (IWAIT), pp. 1\u20132 (2018)","DOI":"10.1109\/IWAIT.2018.8369794"},{"issue":"5","key":"9_CR57","doi-asserted-by":"crossref","first-page":"1149","DOI":"10.1109\/TMI.2018.2794988","volume":"37","author":"L Dai","year":"2018","unstructured":"Dai, L., Fang, R., Li, H., Hou, X., Sheng, B., Wu, Q., Jia, W.: Clinical report guided retinal microaneurysm detection with multi-sieving deep learning. IEEE Trans. Med. Imaging 37(5), 1149\u20131161 (2018)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"9_CR58","doi-asserted-by":"crossref","unstructured":"Harangi, B., Toth, J., Hajdu, A.: Fusion of deep convolutional neural networks for microaneurysm detection in color fundus images. In: 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3705\u20133708 (2018)","DOI":"10.1109\/EMBC.2018.8513035"},{"key":"9_CR59","doi-asserted-by":"crossref","unstructured":"Shan, J., Li, L.: A deep learning method for microaneurysm detection in fundus images. In: 2016 IEEE First International Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE), pp. 357\u2013358 (2016)","DOI":"10.1109\/CHASE.2016.12"},{"key":"9_CR60","unstructured":"Haloi, M.: Improved microaneurysm detection using deep neural network. \nhttps:\/\/arxiv.org\/pdf\/1505.04424.pdf\n\n (2016)"},{"key":"9_CR61","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1016\/j.ins.2017.08.050","volume":"420","author":"JH Tan","year":"2017","unstructured":"Tan, J.H., et al.: Automated segmentation of exudates, haemorrhages, microaneurysms using single convolutional neural network. Inf. Sci. 420, 66\u201376 (2017)","journal-title":"Inf. Sci."},{"issue":"4","key":"9_CR62","doi-asserted-by":"crossref","first-page":"803","DOI":"10.1007\/s10278-012-9549-4","volume":"26","author":"A Tariq","year":"2013","unstructured":"Tariq, A., Akram, M.U., Shaukat, A., Khan, S.A.: Automated detection and grading of diabetic maculopathy in digital retinal images. J. Digit. Imaging 26(4), 803\u2013812 (2013)","journal-title":"J. Digit. Imaging"},{"key":"9_CR63","doi-asserted-by":"crossref","first-page":"175","DOI":"10.3844\/ojbsci.2014.175.180","volume":"14","author":"AGSG Vimala","year":"2014","unstructured":"Vimala, A.G.S.G., Kajamohideen, S.: Detection of diabetic maculopathy in human retinal images using morphological operations. Online J. Biol. Sci. 14, 175\u2013180 (2014)","journal-title":"Online J. Biol. Sci."},{"key":"9_CR64","doi-asserted-by":"crossref","unstructured":"Siddalingaswamy, P.C., Prabhu, K.G.: Automatic grading of diabetic maculopathy severity levels. In: Mahadevappa, M. et al. (eds.) Proceedings of the 2010 International Conference on Systems in Medicine and Biology, pp. 331\u2013334. Excel India Publishers, New Delhi (2010)","DOI":"10.1109\/ICSMB.2010.5735398"},{"key":"9_CR65","doi-asserted-by":"crossref","unstructured":"Hunter, A., Lowell, J. A., Steel, D., Ryder, B., Basu, A.: Automated diagnosis of referable maculopathy in diabetic retinopathy screening. In: Proceedings of 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2011, pp. 3375\u20133378. IEEE, USA (2011)","DOI":"10.1109\/IEMBS.2011.6090914"},{"issue":"12","key":"9_CR66","doi-asserted-by":"crossref","first-page":"2156","DOI":"10.1016\/j.compbiomed.2013.10.003","volume":"43","author":"P Chowriappa","year":"2013","unstructured":"Chowriappa, P., Dua, S., Rajendra, A.U., Muthu, R.K.M.: Ensemble selection for feature- based classification of diabetic maculopathy images. Comput. Biol. Med. 43(12), 2156\u20132162 (2013)","journal-title":"Comput. Biol. Med."},{"key":"9_CR67","volume-title":"Image Processing, Analysis, and Machine Vision","author":"M Sonka","year":"2008","unstructured":"Sonka, M., Hlavac, V., Boyle, R.: Image Processing, Analysis, and Machine Vision. Cengage Learning, United States of America (2008)"},{"issue":"4","key":"9_CR68","doi-asserted-by":"crossref","first-page":"2475","DOI":"10.1109\/TCE.2010.5681130","volume":"56","author":"D Sheet","year":"2010","unstructured":"Sheet, D., Garud, H., Suveer, A., Mahadevappa, M., Chatterjee, J.: Brightness preserving dynamic fuzzy histogram equalization. IEEE Trans. Consum. Electron. 56(4), 2475\u20132480 (2010)","journal-title":"IEEE Trans. Consum. Electron."},{"issue":"3","key":"9_CR69","first-page":"175","volume":"1","author":"S Joshi","year":"2012","unstructured":"Joshi, S., Karule, P.T.: Retinal blood vessel segmentation. Int. J. Eng. Innov. Technol. 1(3), 175\u2013178 (2012)","journal-title":"Int. J. Eng. Innov. Technol."},{"issue":"7","key":"9_CR70","doi-asserted-by":"crossref","first-page":"1198","DOI":"10.3390\/app8071198","volume":"8","author":"S Sreng","year":"2018","unstructured":"Sreng, S., Maneerat, N., Hamamoto, K., Panjaphongse, R.: Automated diabetic retinopathy screening system using hybrid simulated annealing and ensemble bagging classifier. Appl. Sci. 8(7), 1198 (2018)","journal-title":"Appl. Sci."},{"key":"9_CR71","first-page":"1","volume":"521","author":"SS Rahim","year":"2015","unstructured":"Rahim, S.S., Jayne, C., Palade, V., Shuttleworth, J.: Automatic detection of microaneurysms in colour fundus images for diabetic retinopathy screening. J. Neural Comput. Appl. 521, 1\u201316 (2015)","journal-title":"J. Neural Comput. Appl."},{"key":"9_CR72","doi-asserted-by":"crossref","unstructured":"Garud, H., et al.: Brightness preserving contrast enhancement in digital pathology. In: Siddavatan, R., Ghrera, S.P. (eds.) Proceedings of the 2011 International Conference on Image Information Processing (ICIIP 2011), pp. 1\u20135. IEEE, USA (2011)","DOI":"10.1109\/ICIIP.2011.6108964"},{"key":"9_CR73","series-title":"Communications in Computer and Information Science","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1007\/978-3-319-23983-5_7","volume-title":"Engineering Applications of Neural Networks","author":"SS Rahim","year":"2015","unstructured":"Rahim, S.S., Palade, V., Shuttleworth, J., Jayne, C., Omar, R.N.R.: Automatic detection of microaneurysms for diabetic retinopathy screening using fuzzy image processing. In: Iliadis, L., Jayne, C. (eds.) EANN 2015. CCIS, vol. 517, pp. 69\u201379. Springer, Cham (2015). \nhttps:\/\/doi.org\/10.1007\/978-3-319-23983-5_7"},{"issue":"3","key":"9_CR74","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1109\/LSP.2009.2038769","volume":"17","author":"KKV Toh","year":"2010","unstructured":"Toh, K.K.V., Mat Isa, N.A.: Noise adaptive fuzzy switching median filter for salt-and-pepper noise reduction. IEEE Signal Process. Lett. 17(3), 281\u2013284 (2010)","journal-title":"IEEE Signal Process. Lett."},{"key":"9_CR75","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"379","DOI":"10.1007\/978-3-319-23344-4_37","volume-title":"Brain Informatics and Health","author":"SS Rahim","year":"2015","unstructured":"Rahim, S.S., Palade, V., Jayne, C., Holzinger, A., Shuttleworth, J.: Detection of diabetic retinopathy and maculopathy in eye fundus images using fuzzy image processing. In: Guo, Y., Friston, K., Aldo, F., Hill, S., Peng, H. (eds.) BIH 2015. LNCS (LNAI), vol. 9250, pp. 379\u2013388. Springer, Cham (2015). \nhttps:\/\/doi.org\/10.1007\/978-3-319-23344-4_37"},{"key":"9_CR76","series-title":"Communications in Computer and Information Science","doi-asserted-by":"publisher","first-page":"113","DOI":"10.1007\/978-3-319-11071-4_11","volume-title":"Engineering Applications of Neural Networks","author":"SS Rahim","year":"2014","unstructured":"Rahim, S.S., Palade, V., Shuttleworth, J., Jayne, C.: Automatic screening and classification of diabetic retinopathy fundus images. In: Mladenov, V., Jayne, C., Iliadis, L. (eds.) EANN 2014. CCIS, vol. 459, pp. 113\u2013122. Springer, Cham (2014). \nhttps:\/\/doi.org\/10.1007\/978-3-319-11071-4_11"},{"key":"9_CR77","doi-asserted-by":"crossref","unstructured":"Holzinger, A., Langs, G., Denk, H., Zatloukal, K., Mueller, H.: Causability and Explainability of Artificial Intelligence in Medicine. Wiley Interdiscip. Rev. Data Mining Knowl. Discov. 9(4) (2019)","DOI":"10.1002\/widm.1312"},{"key":"9_CR78","doi-asserted-by":"crossref","unstructured":"Holzinger, A., Carrington, A., M\u00fcller, H.: Measuring the Quality of Explanations: The System Causability Scale (SCS). Comparing Human and Machine Explanations. KI - K\u00fcnstliche Intelligenz (German Journal of Artificial intelligence), Special Issue on Interactive Machine Learning, Edited by Kristian Kersting, TU Darmstadt, vol. 34, no. 2, pp. 193\u2013198 (2020)","DOI":"10.1007\/s13218-020-00636-z"}],"container-title":["Lecture Notes in Computer Science","Artificial Intelligence and Machine Learning for Digital Pathology"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-50402-1_9","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,6,23]],"date-time":"2020-06-23T23:07:43Z","timestamp":1592953663000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-50402-1_9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030504014","9783030504021"],"references-count":78,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-50402-1_9","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"24 June 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}}]}}