{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,5]],"date-time":"2026-02-05T05:16:40Z","timestamp":1770268600038,"version":"3.49.0"},"reference-count":20,"publisher":"Walter de Gruyter GmbH","issue":"1","license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,8,20]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>Retinal image analysis is one of the important diagnosis methods in modern ophthalmology because eye information is present in the retina. The image acquisition process may have some effects and can affect the quality of the image. This can be improved by better image enhancement techniques combined with the computer-aided diagnosis system. Deep learning is one of the important computational application techniques used for a medical imaging application. The main aim of this article is to find the best enhancement techniques for the identification of diabetic retinopathy (DR) and are tested with the commonly used deep learning techniques, and the performances are measured. In this article, the input image is taken from the Indian-based database named as Indian Diabetic Retinopathy Image Dataset, and 13 filters are used including smoothing and sharpening filters for enhancing the images. Then, the quality of the enhancement techniques is compared using performance metrics and better results are obtained for Median, Gaussian, Bilateral, Wiener, and partial differential equation filters and are combined for improving the enhancement of images. The output images from all the enhanced filters are given as the convolutional neural network input and the results are compared to find the better enhancement method.<\/jats:p>","DOI":"10.1515\/comp-2020-0177","type":"journal-article","created":{"date-parts":[[2021,8,20]],"date-time":"2021-08-20T22:14:02Z","timestamp":1629497642000},"page":"480-490","source":"Crossref","is-referenced-by-count":14,"title":["Convolutional neural-network-based classification of retinal images with different combinations of filtering techniques"],"prefix":"10.1515","volume":"11","author":[{"given":"Asha Gnana","family":"Priya Henry","sequence":"first","affiliation":[{"name":"Department of Electronics and Communication Engineering, Karunya Institute of Technology and Sciences , Coimbatore 641114 , Tamilnadu , India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Anitha","family":"Jude","sequence":"additional","affiliation":[{"name":"Department of Electronics and Communication Engineering, Karunya Institute of Technology and Sciences , Coimbatore 641114 , Tamilnadu , India"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"374","published-online":{"date-parts":[[2021,8,20]]},"reference":[{"key":"2022020121510294927_j_comp-2020-0177_ref_001","doi-asserted-by":"crossref","unstructured":"H. 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Comput., vol. 6, no. 7. pp. 8729\u20138733, 2016."},{"key":"2022020121510294927_j_comp-2020-0177_ref_005","doi-asserted-by":"crossref","unstructured":"E. Daniel and J. Anitha, \u201cRetinal image enhancement using wavelet domain edge filtering and scaling,\u201d In: Proceedings of International Conference on Electronics and Communication System. IEEE, Coimbatore, India, 2014, pp. 1\u20136.","DOI":"10.1109\/ECS.2014.6892670"},{"key":"2022020121510294927_j_comp-2020-0177_ref_006","doi-asserted-by":"crossref","unstructured":"P. K. Verma, N. P. Singh, and D. Yadav, \u201cImage enhancement: A review,\u201d In: Ambient communications and computer systems. Springer, 2020, pp. 347\u2013355.","DOI":"10.1007\/978-981-15-1518-7_29"},{"key":"2022020121510294927_j_comp-2020-0177_ref_007","doi-asserted-by":"crossref","unstructured":"T. A. Soomro, A. J. Afifi, A. A. Shah, S. Soomro, G. A. Baloch, L. Zheng, et al., \u201cImpact of image enhancement technique on CNN model for retinal blood vessels segmentation,\u201d IEEE Access, vol. 7, pp. 158183\u2013158197, 2019.","DOI":"10.1109\/ACCESS.2019.2950228"},{"key":"2022020121510294927_j_comp-2020-0177_ref_008","doi-asserted-by":"crossref","unstructured":"S. H. Rasta, M. E. Partovi, H. Seyedarabi, and A. Javadzadeh, \u201cA comparative study on preprocessing techniques in diabetic retinopathy retinal images: illumination correction and contrast enhancement,\u201d J. Med. Signals Sens., vol. 5, no. 1. pp. 40\u201348, 2015.","DOI":"10.4103\/2228-7477.150414"},{"key":"2022020121510294927_j_comp-2020-0177_ref_009","doi-asserted-by":"crossref","unstructured":"D. Li, L. Zhang, C. Sun, T. Yin, C. Liu, and J. Yang, \u201cRobust retinal image enhancement via dual-tree complex wavelet transform and morphology-based method,\u201d IEEE Access, vol. 7, pp. 47303\u201347316, 2019.","DOI":"10.1109\/ACCESS.2019.2909788"},{"key":"2022020121510294927_j_comp-2020-0177_ref_010","doi-asserted-by":"crossref","unstructured":"H. Pratt, F. Coenen, D. M. Broadbent, S. P. Harding, and Y. Zheng, \u201cConvolutional neural networks for diabetic retinopathy,\u201d Proc. Comput. Sci., vol. 90, pp. 200\u2013205, 2016.","DOI":"10.1016\/j.procs.2016.07.014"},{"key":"2022020121510294927_j_comp-2020-0177_ref_011","doi-asserted-by":"crossref","unstructured":"A. Subudhi, S. Pattnaik, and S. Sabut, \u201cBlood vessel extraction of diabetic retinopathy using optimized enhanced images and matched filter,\u201d J. Med. Imaging, vol. 3, no. 4. p. 044003, 2016.","DOI":"10.1117\/1.JMI.3.4.044003"},{"key":"2022020121510294927_j_comp-2020-0177_ref_012","unstructured":"N. Singla and N. Singh, \u201cBlood vessel contrast enhancement techniques for retinal images,\u201d Int. J. Adv. Res. Comput. Sci., vol. 8, no. 5. pp. 709\u2013712, 2017."},{"key":"2022020121510294927_j_comp-2020-0177_ref_013","doi-asserted-by":"crossref","unstructured":"Y. Kimori, \u201cMathematical-morphology based approach to the enhancement of morphological features in medical images,\u201d J. Clin. Bioinf., vol. 1, no. 1. p. 33, 2011.","DOI":"10.1186\/2043-9113-1-33"},{"key":"2022020121510294927_j_comp-2020-0177_ref_014","doi-asserted-by":"crossref","unstructured":"S. V. Paranjape, S. Ghosh, A. K. Ray, J. Chatterjee, and A. R. Lande, \u201cA modified fuzzy contrast technique for retinal images,\u201d In: Proceedings of International Conference on Computing Communication Control and Automation. IEEE, Pune, India, 2015, pp. 892\u2013896.","DOI":"10.1109\/ICCUBEA.2015.177"},{"key":"2022020121510294927_j_comp-2020-0177_ref_015","doi-asserted-by":"crossref","unstructured":"M. Arif, G. Wang, O. Geman, and J. Chen, \u201cMedical image segmentation by combining adaptive artificial bee colony and wavelet packet decomposition,\u201d In Proceedings of International Conference on Dependability in Sensor, Cloud and Big Data Systems and Applications. Springer, Singapore, 2019, pp. 158\u2013169.","DOI":"10.1007\/978-981-15-1304-6_13"},{"key":"2022020121510294927_j_comp-2020-0177_ref_016","doi-asserted-by":"crossref","unstructured":"A. Swaminathan, S. S. Ramapackiam, T. Thiraviam, and J. Selvaraj, \u201cContourlet transform-based sharpening enhancement of retinal images and vessel extraction application,\u201d Biomed. Tech.\/Biomed. Eng, vol. 58, no. 1. pp. 87\u201396, 2013.","DOI":"10.1515\/bmt-2012-0055"},{"key":"2022020121510294927_j_comp-2020-0177_ref_017","doi-asserted-by":"crossref","unstructured":"P. Khojasteh, B. Aliahmad, and D. K. 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