Netra :  Stage Specific neural network for early diabetic retinopathy detection using CNN (ResNet50)

Diabetic Retinopathy (DR) is a common complication of diabetes mellitus, which causes lesions onthe retina that affect vision. If it is not detected early, it can lead to blindness. Unfortunately, DRis not a reversible process, and treatment only sustains vision. DR early detection and treatmentcan significantly reduce the risk of vision loss. The manual diagnosis process of DR retina fundusimages by ophthalmologists is time-, effort-, and cost-consuming and prone to misdiagnosis unlikecomputer-aided diagnosis systems.[ 1] Convolutional neural networks are more widely used asa deep learning method in medical image analysis and they are highly effective.[1] Netrascopyis a more efficient system for Diabetic Retinopathy detection, which consists of a low cost,Camera, “DIYretCAM Netrascopy FUNDUS Camera V1”, An Android Application and WebApplication which aims to help patients and doctors detect diabetic retinopathy at early stages bytaking 30-Second video of patient’s retina and passing each frame as an individual test case to aConvolutional Neural Network to detect probability of a patient having diabetic retinopathy.

Content

Author and article information

Journal

Title: ScienceOpen Preprints

Publisher: ScienceOpen

Publication date (Electronic preprint): 22 November 2022

Affiliations

[1 ] Computer Science, University of Delhi, Benito Juarez Marg, South Campus, South Moti Bagh, New Delhi, Delhi 110021;

Author notes

[* ]Email: ansh@ 123456gbsssrajokari.org.in .

Author information

Ansh Tiwari https://orcid.org/0000-0003-2744-0332

Article

DOI: 10.14293/S2199-1006.1.SOR-.PPIGNIW.v1

SO-VID: be64a299-21b0-4d1b-bb17-974b827bdd55

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This work has been published open access under Creative Commons Attribution License CC BY 4.0 , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Conditions, terms of use and publishing policy can be found at www.scienceopen.com .

History

Date received : 22 November 2022

References

Sinthanayothin C., Boyce J. F., Williamson T. H., Cook H. L., Mensah E., Lal S., Usher D.. Automated detection of diabetic retinopathy on digital fundus images. Diabetic Medicine. Vol. 19(2):105–112. 2002. Wiley. [Cross Ref]
Gagnon Langis, Lalonde Marc, Beaulieu Mario, Boucher Marie-Carole. <title>Procedure to detect anatomical structures in optical fundus images</title>. SPIE Proceedings. 2001. SPIE. [Cross Ref]
Sofka M., Stewart C.V.. Erratum to “Retinal vessel centerline extraction using multiscale matched filters, confidence and edge measures”. IEEE Transactions on Medical Imaging. Vol. 26(1)2007. Institute of Electrical and Electronics Engineers (IEEE). [Cross Ref]
Akita Koichiro, Kuga Hideki. A computer method of understanding ocular fundus images. Pattern Recognition. Vol. 15(6):431–443. 1982. Elsevier BV. [Cross Ref]
Goldbaum M., Moezzi S., Taylor A., Chatterjee S., Boyd J., Hunter E., Jain R.. Automated diagnosis and image understanding with object extraction, object classification, and inferencing in retinal images. Proceedings of 3rd IEEE International Conference on Image Processing. IEEE. [Cross Ref]
Bakator Mihalj, Radosav Dragica. Deep Learning and Medical Diagnosis: A Review of Literature. Multimodal Technologies and Interaction. Vol. 2(3)2018. MDPI AG. [Cross Ref]
Li Helen K., Horton Mark, Bursell Sven-Erik, Cavallerano Jerry, Zimmer-Galler Ingrid, Tennant Mathew, Abramoff Michael, Chaum Edward, DeBuc Debra Cabrera, Leonard-Martin Tom, Winchester Marc. Telehealth Practice Recommendations for Diabetic Retinopathy, Second Edition. Telemedicine and e-Health. Vol. 17(10):814–837. 2011. Mary Ann Liebert Inc. [Cross Ref]
Dubow Michael, Pinhas Alexander, Shah Nishit, Cooper Robert F., Gan Alexander, Gentile Ronald C., Hendrix Vernon, Sulai Yusufu N., Carroll Joseph, Chui Toco Y. P., Walsh Joseph B., Weitz Rishard, Dubra Alfredo, Rosen Richard B.. Classification of Human Retinal Microaneurysms Using Adaptive Optics Scanning Light Ophthalmoscope Fluorescein Angiography. Investigative Opthalmology & Visual Science. Vol. 55(3)2014. Association for Research in Vision and Ophthalmology (ARVO). [Cross Ref]
Dubow Michael, Pinhas Alexander, Shah Nishit, Cooper Robert F., Gan Alexander, Gentile Ronald C., Hendrix Vernon, Sulai Yusufu N., Carroll Joseph, Chui Toco Y. P., Walsh Joseph B., Weitz Rishard, Dubra Alfredo, Rosen Richard B.. Classification of Human Retinal Microaneurysms Using Adaptive Optics Scanning Light Ophthalmoscope Fluorescein Angiography. Investigative Opthalmology & Visual Science. Vol. 55(3)2014. Association for Research in Vision and Ophthalmology (ARVO). [Cross Ref]
Scotland G. S., McNamee P., Fleming A. D., Goatman K. A., Philip S., Prescott G. J., Sharp P. F., Williams G. J., Wykes W., Leese G. P., Olson J. A.. Costs and consequences of automated algorithms versus manual grading for the detection of referable diabetic retinopathy. British Journal of Ophthalmology. Vol. 94(6):712–719. 2010. BMJ. [Cross Ref]
Pires Dias João Miguel, Oliveira Carlos Manta, da Silva Cruz Luís A.. Retinal image quality assessment using generic image quality indicators. Information Fusion. Vol. 19:73–90. 2014. Elsevier BV. [Cross Ref]
Decencière Etienne, Zhang Xiwei, Cazuguel Guy, Lay Bruno, Cochener Béatrice, Trone Caroline, Gain Philippe, Ordonez Richard, Massin Pascale, Erginay Ali, Charton Béatrice, Klein Jean-Claude. FEEDBACK ON A PUBLICLY DISTRIBUTED IMAGE DATABASE: THE MESSIDOR DATABASE. Image Analysis & Stereology. Vol. 33(3)2014. Slovenian Society for Stereology and Quantitative Image Analysis. [Cross Ref]
Decencière E., Cazuguel G., Zhang X., Thibault G., Klein J.-C., Meyer F., Marcotegui B., Quellec G., Lamard M., Danno R., Elie D., Massin P., Viktor Z., Erginay A., Laÿ B., Chabouis A.. TeleOphta: Machine learning and image processing methods for teleophthalmology. IRBM. Vol. 34(2):196–203. 2013. Elsevier BV. [Cross Ref]
Dai Ling, Wu Liang, Li Huating, Cai Chun, Wu Qiang, Kong Hongyu, Liu Ruhan, Wang Xiangning, Hou Xuhong, Liu Yuexing, Long Xiaoxue, Wen Yang, Lu Lina, Shen Yaxin, Chen Yan, Shen Dinggang, Yang Xiaokang, Zou Haidong, Sheng Bin, Jia Weiping. A deep learning system for detecting diabetic retinopathy across the disease spectrum. Nature Communications. Vol. 12(1)2021. Springer Science and Business Media LLC. [Cross Ref]
Tran Kenneth, Mendel Thomas A., Holbrook Kristina L., Yates Paul A.. Construction of an Inexpensive, Hand-Held Fundus Camera through Modification of a Consumer “Point-and-Shoot” Camera. Investigative Opthalmology & Visual Science. Vol. 53(12)2012. Association for Research in Vision and Ophthalmology (ARVO). [Cross Ref]

Comments

Comment on this article

[1] Sinthanayothin C., Boyce J. F., Williamson T. H., Cook H. L., Mensah E., Lal S., Usher D.. Automated detection of diabetic retinopathy on digital fundus images. Diabetic Medicine. Vol. 19(2):105–112. 2002. Wiley. [Cross Ref]

[2] Gagnon Langis, Lalonde Marc, Beaulieu Mario, Boucher Marie-Carole. <title>Procedure to detect anatomical structures in optical fundus images</title>. SPIE Proceedings. 2001. SPIE. [Cross Ref]

[3] Sofka M., Stewart C.V.. Erratum to “Retinal vessel centerline extraction using multiscale matched filters, confidence and edge measures”. IEEE Transactions on Medical Imaging. Vol. 26(1)2007. Institute of Electrical and Electronics Engineers (IEEE). [Cross Ref]

[4] Akita Koichiro, Kuga Hideki. A computer method of understanding ocular fundus images. Pattern Recognition. Vol. 15(6):431–443. 1982. Elsevier BV. [Cross Ref]

[5] Goldbaum M., Moezzi S., Taylor A., Chatterjee S., Boyd J., Hunter E., Jain R.. Automated diagnosis and image understanding with object extraction, object classification, and inferencing in retinal images. Proceedings of 3rd IEEE International Conference on Image Processing. IEEE. [Cross Ref]

[6] Bakator Mihalj, Radosav Dragica. Deep Learning and Medical Diagnosis: A Review of Literature. Multimodal Technologies and Interaction. Vol. 2(3)2018. MDPI AG. [Cross Ref]

[7] Li Helen K., Horton Mark, Bursell Sven-Erik, Cavallerano Jerry, Zimmer-Galler Ingrid, Tennant Mathew, Abramoff Michael, Chaum Edward, DeBuc Debra Cabrera, Leonard-Martin Tom, Winchester Marc. Telehealth Practice Recommendations for Diabetic Retinopathy, Second Edition. Telemedicine and e-Health. Vol. 17(10):814–837. 2011. Mary Ann Liebert Inc. [Cross Ref]

[8] Dubow Michael, Pinhas Alexander, Shah Nishit, Cooper Robert F., Gan Alexander, Gentile Ronald C., Hendrix Vernon, Sulai Yusufu N., Carroll Joseph, Chui Toco Y. P., Walsh Joseph B., Weitz Rishard, Dubra Alfredo, Rosen Richard B.. Classification of Human Retinal Microaneurysms Using Adaptive Optics Scanning Light Ophthalmoscope Fluorescein Angiography. Investigative Opthalmology & Visual Science. Vol. 55(3)2014. Association for Research in Vision and Ophthalmology (ARVO). [Cross Ref]

[9] Dubow Michael, Pinhas Alexander, Shah Nishit, Cooper Robert F., Gan Alexander, Gentile Ronald C., Hendrix Vernon, Sulai Yusufu N., Carroll Joseph, Chui Toco Y. P., Walsh Joseph B., Weitz Rishard, Dubra Alfredo, Rosen Richard B.. Classification of Human Retinal Microaneurysms Using Adaptive Optics Scanning Light Ophthalmoscope Fluorescein Angiography. Investigative Opthalmology & Visual Science. Vol. 55(3)2014. Association for Research in Vision and Ophthalmology (ARVO). [Cross Ref]

[10] Scotland G. S., McNamee P., Fleming A. D., Goatman K. A., Philip S., Prescott G. J., Sharp P. F., Williams G. J., Wykes W., Leese G. P., Olson J. A.. Costs and consequences of automated algorithms versus manual grading for the detection of referable diabetic retinopathy. British Journal of Ophthalmology. Vol. 94(6):712–719. 2010. BMJ. [Cross Ref]

[11] Pires Dias João Miguel, Oliveira Carlos Manta, da Silva Cruz Luís A.. Retinal image quality assessment using generic image quality indicators. Information Fusion. Vol. 19:73–90. 2014. Elsevier BV. [Cross Ref]

[12] Decencière Etienne, Zhang Xiwei, Cazuguel Guy, Lay Bruno, Cochener Béatrice, Trone Caroline, Gain Philippe, Ordonez Richard, Massin Pascale, Erginay Ali, Charton Béatrice, Klein Jean-Claude. FEEDBACK ON A PUBLICLY DISTRIBUTED IMAGE DATABASE: THE MESSIDOR DATABASE. Image Analysis & Stereology. Vol. 33(3)2014. Slovenian Society for Stereology and Quantitative Image Analysis. [Cross Ref]

[13] Decencière E., Cazuguel G., Zhang X., Thibault G., Klein J.-C., Meyer F., Marcotegui B., Quellec G., Lamard M., Danno R., Elie D., Massin P., Viktor Z., Erginay A., Laÿ B., Chabouis A.. TeleOphta: Machine learning and image processing methods for teleophthalmology. IRBM. Vol. 34(2):196–203. 2013. Elsevier BV. [Cross Ref]

[14] Dai Ling, Wu Liang, Li Huating, Cai Chun, Wu Qiang, Kong Hongyu, Liu Ruhan, Wang Xiangning, Hou Xuhong, Liu Yuexing, Long Xiaoxue, Wen Yang, Lu Lina, Shen Yaxin, Chen Yan, Shen Dinggang, Yang Xiaokang, Zou Haidong, Sheng Bin, Jia Weiping. A deep learning system for detecting diabetic retinopathy across the disease spectrum. Nature Communications. Vol. 12(1)2021. Springer Science and Business Media LLC. [Cross Ref]

[15] Tran Kenneth, Mendel Thomas A., Holbrook Kristina L., Yates Paul A.. Construction of an Inexpensive, Hand-Held Fundus Camera through Modification of a Consumer “Point-and-Shoot” Camera. Investigative Opthalmology & Visual Science. Vol. 53(12)2012. Association for Research in Vision and Ophthalmology (ARVO). [Cross Ref]

Netra : Stage Specific neural network for early diabetic retinopathy detection using CNN (ResNet50)