Fundus Eye Images Classification for Diabetic Retinopathy Detection Using Very Deep Convolutional Neural Network
Resumo
Diabetic retinopathy is an anomaly responsible for causing microvascular and macrovascular damage to the retina and occurs as a consequence of the worsening of diabetes. According to the World Health Organization (WHO), diabetic retinopathy is the most common cause of avoidable blindness in patients with diabetes worldwide. Early detection is important for the efficiency of treatments. Fundus Eye Image can be used to identify early disease development and monitor the patient’s clinical condition. The diagnostic process using this type of image may require some expertise from the ophthalmologist since not all retina anomalies are clearly visible. Thus, this paper proposes the development of a classification method based on Convolutional Neural Networks, but highly dense and deeper. The proposed method obtained a total of 92% AUC in the given experiments.Referências
Data Science Academy. Deep learning book, 2019. Disponível em: http://www.deeplearningbook.com.br/. Acesso em: 15/03/2020.
IDF Diabetes Atlas. 9th, 2019.
Kierstan Boyd. Diabetic retinopathy diagnosis, 2019. Disponível em: https://www.aao.org/eye-health/diseases/diabetic-retinopathy-diagnosis. Acesso em: 08/03/2020.
Sociedade Brasileira de Diabetes. Diretrizes da sociedade brasileira de diabetes, 2020. Disponível em: https://www.diabetes.org.br/. Acesso em: 20/03/2020.
Secretaria de Saúde do Estado do Paraná. Linha guia de diabetes mellitus, 2018., 2018. Disponível em: http://www.saude.pr.gov.br/arquivos/File/linhaguiadiabetes2018.pdf. Acesso em: 08/03/2020.
Etienne Decenciere, Guy Cazuguel, Xiwei Zhang, Guillaume Thibault, J-C Klein, Fernand Meyer, Beatriz Marcotegui, Gwénolé Quellec, Mathieu Lamard, Ronan Danno, et al. Teleophta: Machine learning and image processing methods for teleophthalmology. Irbm, 34(2):196–203, 2013.
Etienne Decenciere, Xiwei Zhang, Guy Cazuguel, Bruno Lay, Béatrice Cochener, Caroline Trone, Philippe Gain, Richard Ordonez, Pascale Massin, Ali Erginay, et al. Feedback on a publicly distributed image database: the messidor database. Image Analysis & Stereology, 33(3):231–234, 2014.
BS Divya, Kamalraj Subramaniam, and HR Nanjundaswamy. Human epithelial type-2 cell image classification using an artificial neural network with hybrid descriptors. IETE Journal of Research, 66(1):30– 41, 2020.
Hospital dos Olhos. Informação: Retinopatia diabética., 2019. Disponível em: https://www.sadalla.com.br/index/especialidadesoftalmologia/retina-tratamento-cirurgia/. Acesso em: 08/03/2020.
Frederick L Ferris. How effective are treatments for diabetic retinopathy? Jama, 269(10):1290–1291, 1993.
SS Furuie, MA Gutierrez, NB Bertozzo, JCB Figueriedo, and M Yamaguti. Archiving and retrieving long-term cineangiographic images in a pacs. In Computers in Cardiology 1999. Vol. 26 (Cat. No. 99CH37004), pages 435–438. IEEE, 1999.
Rishab Gargeya and Theodore Leng. Automated identication of diabetic retinopathy using deep learning. Ophthalmology, 124(7):962– 969, 2017.
R Klein. Epidemiology of eye disease in diabetes. Diabetes and Ocular Disease, 2000.
Ronald Klein, Barbara EK Klein, and Scot E Moss. Visual impairment in diabetes. Ophthalmology, 91(1):1–9, 1984.
RS Marques. Segmentação automática das mamas em imagens térmicas. Master's thesis, Instituto de Computação, Universidade Federal Fluminense, Niterói, RJ, Brasil, 2012.
A Nithya, Ahilan Appathurai, N Venkatadri, DR Ramji, and C Anna Palagan. Kidney disease detection and segmentation using articial neural network and multi-kernel k-means clustering for ultrasound images. Measurement, 149:106952, 2020.
Prasanna Porwal, Samiksha Pachade, Ravi Kamble, Manesh Kokare, Girish Deshmukh, Vivek Sahasrabuddhe, and Fabrice Meriaudeau. Indian diabetic retinopathy image dataset (idrid): A database for diabetic retinopathy screening research. Data, 3(3):25, 2018.
Harry Pratt, Frans Coenen, Deborah M Broadbent, Simon P Harding, and Yalin Zheng. Convolutional neural networks for diabetic retinopathy. Procedia Computer Science, 90:200–205, 2016.
Prachi R Rajarapollu, Debashis Adhikari, and Nutan V Bansode. Use of articial neural network for abnormality detection in medical images. In Optimization in Machine Learning and Applications, pages 1–12. Springer, 2020.
Poonam M Rokade and Ramesh R Manza. Automatic detection of hard exudates in retinal images using haar wavelet transform. eye, 4(5):402– 410, 2015.
Karen Simonyan and Andrew Zisserman. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556, 2014.
Chanjira Sinthanayothin, James F Boyce, Tom H Williamson, Helen L Cook, Evelyn Mensah, Shantanu Lal, and David Usher. Automated detection of diabetic retinopathy on digital fundus images. Diabetic medicine, 19(2):105–112, 2002.
Kanika Verma, Prakash Deep, and AG Ramakrishnan. Detection and classification of diabetic retinopathy using retinal images. In 2011 Annual IEEE India Conference, pages 1–6. IEEE, 2011.
Guangfen Wei, Gang Li, Jie Zhao, and Aixiang He. Development of a lenet-5 gas identication cnn structure for electronic noses. Sensors, 19(1):217, 2019.
Daniel Welfer, Jacob Scharcanski, and Diane Ruschel Marinho. A coarse-to-ne strategy for automatically detecting exudates in color eye fundus images. computerized medical imaging and graphics, 34(3):228– 235, 2010.
Doaa Youssef and Nahed H Solouma. Accurate detection of blood vessels improves the detection of exudates in color fundus images. Computer methods and programs in biomedicine, 108(3):1052–1061, 2012.
Yufeng Zheng, Clifford Yang, and Alex Merkulov. Breast cancer screening using convolutional neural network and follow-up digital In Computational Imaging III, volume 10669, page mammography 1066905. International Society for Optics and Photonics, 2018.
Karel Zuiderveld. Contrast limited adaptive histogram equalization. In Graphics gems IV, pages 474–485. Academic Press Professional, Inc., 1994.
IDF Diabetes Atlas. 9th, 2019.
Kierstan Boyd. Diabetic retinopathy diagnosis, 2019. Disponível em: https://www.aao.org/eye-health/diseases/diabetic-retinopathy-diagnosis. Acesso em: 08/03/2020.
Sociedade Brasileira de Diabetes. Diretrizes da sociedade brasileira de diabetes, 2020. Disponível em: https://www.diabetes.org.br/. Acesso em: 20/03/2020.
Secretaria de Saúde do Estado do Paraná. Linha guia de diabetes mellitus, 2018., 2018. Disponível em: http://www.saude.pr.gov.br/arquivos/File/linhaguiadiabetes2018.pdf. Acesso em: 08/03/2020.
Etienne Decenciere, Guy Cazuguel, Xiwei Zhang, Guillaume Thibault, J-C Klein, Fernand Meyer, Beatriz Marcotegui, Gwénolé Quellec, Mathieu Lamard, Ronan Danno, et al. Teleophta: Machine learning and image processing methods for teleophthalmology. Irbm, 34(2):196–203, 2013.
Etienne Decenciere, Xiwei Zhang, Guy Cazuguel, Bruno Lay, Béatrice Cochener, Caroline Trone, Philippe Gain, Richard Ordonez, Pascale Massin, Ali Erginay, et al. Feedback on a publicly distributed image database: the messidor database. Image Analysis & Stereology, 33(3):231–234, 2014.
BS Divya, Kamalraj Subramaniam, and HR Nanjundaswamy. Human epithelial type-2 cell image classification using an artificial neural network with hybrid descriptors. IETE Journal of Research, 66(1):30– 41, 2020.
Hospital dos Olhos. Informação: Retinopatia diabética., 2019. Disponível em: https://www.sadalla.com.br/index/especialidadesoftalmologia/retina-tratamento-cirurgia/. Acesso em: 08/03/2020.
Frederick L Ferris. How effective are treatments for diabetic retinopathy? Jama, 269(10):1290–1291, 1993.
SS Furuie, MA Gutierrez, NB Bertozzo, JCB Figueriedo, and M Yamaguti. Archiving and retrieving long-term cineangiographic images in a pacs. In Computers in Cardiology 1999. Vol. 26 (Cat. No. 99CH37004), pages 435–438. IEEE, 1999.
Rishab Gargeya and Theodore Leng. Automated identication of diabetic retinopathy using deep learning. Ophthalmology, 124(7):962– 969, 2017.
R Klein. Epidemiology of eye disease in diabetes. Diabetes and Ocular Disease, 2000.
Ronald Klein, Barbara EK Klein, and Scot E Moss. Visual impairment in diabetes. Ophthalmology, 91(1):1–9, 1984.
RS Marques. Segmentação automática das mamas em imagens térmicas. Master's thesis, Instituto de Computação, Universidade Federal Fluminense, Niterói, RJ, Brasil, 2012.
A Nithya, Ahilan Appathurai, N Venkatadri, DR Ramji, and C Anna Palagan. Kidney disease detection and segmentation using articial neural network and multi-kernel k-means clustering for ultrasound images. Measurement, 149:106952, 2020.
Prasanna Porwal, Samiksha Pachade, Ravi Kamble, Manesh Kokare, Girish Deshmukh, Vivek Sahasrabuddhe, and Fabrice Meriaudeau. Indian diabetic retinopathy image dataset (idrid): A database for diabetic retinopathy screening research. Data, 3(3):25, 2018.
Harry Pratt, Frans Coenen, Deborah M Broadbent, Simon P Harding, and Yalin Zheng. Convolutional neural networks for diabetic retinopathy. Procedia Computer Science, 90:200–205, 2016.
Prachi R Rajarapollu, Debashis Adhikari, and Nutan V Bansode. Use of articial neural network for abnormality detection in medical images. In Optimization in Machine Learning and Applications, pages 1–12. Springer, 2020.
Poonam M Rokade and Ramesh R Manza. Automatic detection of hard exudates in retinal images using haar wavelet transform. eye, 4(5):402– 410, 2015.
Karen Simonyan and Andrew Zisserman. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556, 2014.
Chanjira Sinthanayothin, James F Boyce, Tom H Williamson, Helen L Cook, Evelyn Mensah, Shantanu Lal, and David Usher. Automated detection of diabetic retinopathy on digital fundus images. Diabetic medicine, 19(2):105–112, 2002.
Kanika Verma, Prakash Deep, and AG Ramakrishnan. Detection and classification of diabetic retinopathy using retinal images. In 2011 Annual IEEE India Conference, pages 1–6. IEEE, 2011.
Guangfen Wei, Gang Li, Jie Zhao, and Aixiang He. Development of a lenet-5 gas identication cnn structure for electronic noses. Sensors, 19(1):217, 2019.
Daniel Welfer, Jacob Scharcanski, and Diane Ruschel Marinho. A coarse-to-ne strategy for automatically detecting exudates in color eye fundus images. computerized medical imaging and graphics, 34(3):228– 235, 2010.
Doaa Youssef and Nahed H Solouma. Accurate detection of blood vessels improves the detection of exudates in color fundus images. Computer methods and programs in biomedicine, 108(3):1052–1061, 2012.
Yufeng Zheng, Clifford Yang, and Alex Merkulov. Breast cancer screening using convolutional neural network and follow-up digital In Computational Imaging III, volume 10669, page mammography 1066905. International Society for Optics and Photonics, 2018.
Karel Zuiderveld. Contrast limited adaptive histogram equalization. In Graphics gems IV, pages 474–485. Academic Press Professional, Inc., 1994.
Publicado
07/10/2020
Como Citar
GAMA, Ítalo; COELHO, Alessandra; BAFFA, Matheus.
Fundus Eye Images Classification for Diabetic Retinopathy Detection Using Very Deep Convolutional Neural Network. In: WORKSHOP DE VISÃO COMPUTACIONAL (WVC), 16. , 2020, Evento Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2020
.
p. 24-29.
DOI: https://doi.org/10.5753/wvc.2020.13497.
