Analysis of pre-trained convolutional neural network models in diabetic retinopathy detection through retinal fundus images
Diabetic Retinopathy (DR) is a disease on the rise; as this is a complication of diabetes, it becomes an imminent fate in people who have not been treated correctly for the disease, resulting in possible loss of vision if not is detected in time. This disease affects the retina, and the diagnosis is...
- Autores:
-
Escorcia-Gutierrez, Jose
Cuello, Jose
Barraza, Carlos
Gamarra, Margarita
Romero-Aroca, Pedro
CAICEDO BRAVO, EDUARDO
Puig, Domenec
- Tipo de recurso:
- Part of book
- Fecha de publicación:
- 2022
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/9481
- Acceso en línea:
- https://hdl.handle.net/11323/9481
https://doi.org/10.1007/978-3-031-10539-5_15
https://repositorio.cuc.edu.co/
- Palabra clave:
- Diabetic retinopathy
Retinal imaging
Image recognition
Convolutional neural network
Transfer learning
- Rights
- openAccess
- License
- © 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
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Analysis of pre-trained convolutional neural network models in diabetic retinopathy detection through retinal fundus images |
| title |
Analysis of pre-trained convolutional neural network models in diabetic retinopathy detection through retinal fundus images |
| spellingShingle |
Analysis of pre-trained convolutional neural network models in diabetic retinopathy detection through retinal fundus images Diabetic retinopathy Retinal imaging Image recognition Convolutional neural network Transfer learning |
| title_short |
Analysis of pre-trained convolutional neural network models in diabetic retinopathy detection through retinal fundus images |
| title_full |
Analysis of pre-trained convolutional neural network models in diabetic retinopathy detection through retinal fundus images |
| title_fullStr |
Analysis of pre-trained convolutional neural network models in diabetic retinopathy detection through retinal fundus images |
| title_full_unstemmed |
Analysis of pre-trained convolutional neural network models in diabetic retinopathy detection through retinal fundus images |
| title_sort |
Analysis of pre-trained convolutional neural network models in diabetic retinopathy detection through retinal fundus images |
| dc.creator.fl_str_mv |
Escorcia-Gutierrez, Jose Cuello, Jose Barraza, Carlos Gamarra, Margarita Romero-Aroca, Pedro CAICEDO BRAVO, EDUARDO Puig, Domenec |
| dc.contributor.author.spa.fl_str_mv |
Escorcia-Gutierrez, Jose Cuello, Jose Barraza, Carlos Gamarra, Margarita Romero-Aroca, Pedro CAICEDO BRAVO, EDUARDO Puig, Domenec |
| dc.subject.proposal.eng.fl_str_mv |
Diabetic retinopathy Retinal imaging Image recognition Convolutional neural network Transfer learning |
| topic |
Diabetic retinopathy Retinal imaging Image recognition Convolutional neural network Transfer learning |
| description |
Diabetic Retinopathy (DR) is a disease on the rise; as this is a complication of diabetes, it becomes an imminent fate in people who have not been treated correctly for the disease, resulting in possible loss of vision if not is detected in time. This disease affects the retina, and the diagnosis is made based on fundus images of patients, through which various lesions and anomalies can be visualized. Visual inspection is a challenging task, and the diagnosis is expert dependent. This article proposes a convolutional neural network (CNN) model to detect DR, a common illness in diabetic patients. This work allows estimating the capacity of a pre-trained CNN (VGG16) using the transfer learning technique to detect symptoms and injuries caused by DR. For learning and feature extraction we used a set of retinal images obtained from the APTOS 2019 Blindness Detection competition in Kaggle. This network is trained and learns to identify between healthy retina and RD with high performance, overcoming other works. The best experimentation we obtained reached an accuracy value of 96.86% for DR detection tasks. |
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2022 |
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2022 |
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Escorcia-Gutierrez, J. et al. (2022). Analysis of Pre-trained Convolutional Neural Network Models in Diabetic Retinopathy Detection Through Retinal Fundus Images. In: Saeed, K., Dvorský, J. (eds) Computer Information Systems and Industrial Management. CISIM 2022. Lecture Notes in Computer Science, vol 13293. Springer, Cham. https://doi.org/10.1007/978-3-031-10539-5_15 |
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978-3-031-10538-8 |
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https://hdl.handle.net/11323/9481 |
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Escorcia-Gutierrez, J. et al. (2022). Analysis of Pre-trained Convolutional Neural Network Models in Diabetic Retinopathy Detection Through Retinal Fundus Images. In: Saeed, K., Dvorský, J. (eds) Computer Information Systems and Industrial Management. CISIM 2022. Lecture Notes in Computer Science, vol 13293. Springer, Cham. https://doi.org/10.1007/978-3-031-10539-5_15 978-3-031-10538-8 10.1007/978-3-031-10539-5_15 Corporación Universidad de la Costa REDICUC - Repositorio CUC 978-3-031-10539-5 |
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Romero-Aroca, P., et al.: Cost of diabetic retinopathy and macular oedema in a population, an eight year follow up. BMC Ophthalmol. 16, 1–7 (2016). https://doi.org/10.1186/S12886-016-0318-X Pelullo, C.P., Rossiello, R., Nappi, R., Napolitano, F., Di Giuseppe, G.: Diabetes prevention: knowledge and perception of risk among italian population. Biomed. Res. Int. 2019 (2019). https://doi.org/10.1155/2019/2753131 Thapa, R., et al.: Prevalence and risk factors of diabetic retinopathy among an elderly population with diabetes in Nepal: the Bhaktapur Retina Study. Clin. Ophthalmol. 12, 561 (2018). https://doi.org/10.2147/OPTH.S157560 Sneha, N., Gangil, T.: Analysis of diabetes mellitus for early prediction using optimal features selection. J. Big Data 6(1), 1–19 (2019). https://doi.org/10.1186/s40537-019-0175-6 Diabetes. https://www.who.int/news-room/fact-sheets/detail/diabetes. Accessed 21 Feb 2022 Wang, Y., Wang, G.A., Fan, W., Li, J.: A deep learning based pipeline for image grading of diabetic retinopathy. In: Chen, H., Fang, Q., Zeng, D., Wu, J. (eds.) ICSH 2018. LNCS (LNAI and LNB), vol. 10983, pp. 240–248. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-03649-2_24 Muthumayil, K., Manikandan, S., Srinivasan, S., Escorcia-Gutierrez, J., Gamarra, M., Mansour, R.F.: Diagnosis of leukemia disease based on enhanced virtual neural network. Comput. Mater. Contin. 69, 2031–2044 (2021). https://doi.org/10.32604/CMC.2021.017116 Orlando, J.I., Prokofyeva, E., Del Fresno, M., Blaschko, M.B.: An ensemble deep learning based approach for red lesion detection in fundus images. Comput. Methods Program. Biomed. 153, 115–127 (2017) Bodapati, J.D., Shaik, N.S., Naralasetti, V.: Composite deep neural network with gated-attention mechanism for diabetic retinopathy severity classification. J. Ambient. Intell. Humaniz. Comput. 12(10), 9825–9839 (2021). https://doi.org/10.1007/s12652-020-02727-z Adriman, R., Muchtar, K., Maulina, N.: Performance evaluation of binary classification of diabetic retinopathy through deep learning techniques using texture feature. Proc. Comput. Sci. 179, 88–94 (2021). https://doi.org/10.1016/j.procs.2020.12.012 Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, pp. 2261–2269. Institute of Electrical and Electronics Engineers Inc. (2017). https://doi.org/10.1109/CVPR.2017.243 He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 770–778. IEEE Computer Society (2016). https://doi.org/10.1109/CVPR.2016.90 Khalifa, N.E.M., Loey, M., Taha, M.H.N., Mohamed, H.N.E.T.: Deep transfer learning models for medical diabetic retinopathy detection. Acta Inform. Medica. 27, 327–332 (2019). https://doi.org/10.5455/aim.2019.27.327-332 VGG16 - Convolutional Network for Classification and Detection VGG-19 convolutional neural network - MATLAB vgg19 Gangwar, A.K., Ravi, V.: Diabetic retinopathy detection using transfer learning and deep learning. Presented at the (2021). https://doi.org/10.1007/978-981-15-5788-0_64 Google AI Blog: Improving Inception and Image Classification in TensorFlow Google colab is a free cloud notebook environment. https://bcrf.biochem.wisc.edu/2021/02/05/google-colab-is-a-free-cloud-notebook-environment/#:~:text=Google. Colab is a free cloud-based service that allows, and install new python libraries. &text=Colab is heavily used for, a platform to learn Python APTOS 2019 Blindness Detection | Kaggle APTOS: Eye Preprocessing in Diabetic Retinopathy | Kaggle Torres, J.: Deep learning, introducción práctica con Keras (SEGUNDA PARTE) - Jordi TORRES.AI Keras: The Python deep learning API. https://keras.io/ Montereal, Q.: APTOS 2019: DenseNet Keras Starter | Kaggle Kassani, S.H., Kassani, P.H., Khazaeinezhad, R., Wesolowski, M.J., Schneider, K.A., Deters, R.: Diabetic retinopathy classification using a modified xception architecture. In: 2019 IEEE 19th International Symposium on Signal Processing and Information, ISSPIT 2019, pp. 1–6 (2019). https://doi.org/10.1109/ISSPIT47144.2019.9001846 Cuello Navarro, J., Barraza Peña, C., Escorcia-Gutiérrez, J.: Una revisión de los métodos de deep learning aplicados a la detección automatizada de la retinopatía diabética. Rev. SEXTANTE 23, 14–33 (2020) Bodapati, J.D., Shaik, N.S., Naralasetti, V.: Deep convolution feature aggregation: an application to diabetic retinopathy severity level prediction. Signal Image Video Process. 15(5), 923–930 (2021). https://doi.org/10.1007/s11760-020-01816-y Dekhil, O., Naglah, A., Shaban, M., Ghazal, M., Taher, F., Elbaz, A.: Deep learning based method for computer aided diagnosis of diabetic retinopathy. In: IST 2019 - IEEE International Conference on Imaging Systems and Techniques Proceedings, pp. 19–22 (2019). https://doi.org/10.1109/IST48021.2019.9010333 |
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Escorcia-Gutierrez, JoseCuello, JoseBarraza, CarlosGamarra, MargaritaRomero-Aroca, PedroCAICEDO BRAVO, EDUARDOPuig, Domenec2022-08-30T13:51:56Z2022-08-30T13:51:56Z2022Escorcia-Gutierrez, J. et al. (2022). Analysis of Pre-trained Convolutional Neural Network Models in Diabetic Retinopathy Detection Through Retinal Fundus Images. In: Saeed, K., Dvorský, J. (eds) Computer Information Systems and Industrial Management. CISIM 2022. Lecture Notes in Computer Science, vol 13293. Springer, Cham. https://doi.org/10.1007/978-3-031-10539-5_15978-3-031-10538-8https://hdl.handle.net/11323/9481https://doi.org/10.1007/978-3-031-10539-5_1510.1007/978-3-031-10539-5_15Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/978-3-031-10539-5Diabetic Retinopathy (DR) is a disease on the rise; as this is a complication of diabetes, it becomes an imminent fate in people who have not been treated correctly for the disease, resulting in possible loss of vision if not is detected in time. This disease affects the retina, and the diagnosis is made based on fundus images of patients, through which various lesions and anomalies can be visualized. Visual inspection is a challenging task, and the diagnosis is expert dependent. This article proposes a convolutional neural network (CNN) model to detect DR, a common illness in diabetic patients. This work allows estimating the capacity of a pre-trained CNN (VGG16) using the transfer learning technique to detect symptoms and injuries caused by DR. For learning and feature extraction we used a set of retinal images obtained from the APTOS 2019 Blindness Detection competition in Kaggle. This network is trained and learns to identify between healthy retina and RD with high performance, overcoming other works. The best experimentation we obtained reached an accuracy value of 96.86% for DR detection tasks.1 páginaapplication/pdfengSpringer NatureSwitzerlandLecture Notes in Computer Science;Computer Information Systems and Industrial Management0Romero-Aroca, P., et al.: Cost of diabetic retinopathy and macular oedema in a population, an eight year follow up. BMC Ophthalmol. 16, 1–7 (2016). https://doi.org/10.1186/S12886-016-0318-XPelullo, C.P., Rossiello, R., Nappi, R., Napolitano, F., Di Giuseppe, G.: Diabetes prevention: knowledge and perception of risk among italian population. Biomed. Res. Int. 2019 (2019). https://doi.org/10.1155/2019/2753131Thapa, R., et al.: Prevalence and risk factors of diabetic retinopathy among an elderly population with diabetes in Nepal: the Bhaktapur Retina Study. Clin. Ophthalmol. 12, 561 (2018). https://doi.org/10.2147/OPTH.S157560Sneha, N., Gangil, T.: Analysis of diabetes mellitus for early prediction using optimal features selection. J. Big Data 6(1), 1–19 (2019). https://doi.org/10.1186/s40537-019-0175-6 Diabetes. https://www.who.int/news-room/fact-sheets/detail/diabetes. Accessed 21 Feb 2022Wang, Y., Wang, G.A., Fan, W., Li, J.: A deep learning based pipeline for image grading of diabetic retinopathy. In: Chen, H., Fang, Q., Zeng, D., Wu, J. (eds.) ICSH 2018. LNCS (LNAI and LNB), vol. 10983, pp. 240–248. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-03649-2_24Muthumayil, K., Manikandan, S., Srinivasan, S., Escorcia-Gutierrez, J., Gamarra, M., Mansour, R.F.: Diagnosis of leukemia disease based on enhanced virtual neural network. Comput. Mater. Contin. 69, 2031–2044 (2021). https://doi.org/10.32604/CMC.2021.017116Orlando, J.I., Prokofyeva, E., Del Fresno, M., Blaschko, M.B.: An ensemble deep learning based approach for red lesion detection in fundus images. Comput. Methods Program. Biomed. 153, 115–127 (2017)Bodapati, J.D., Shaik, N.S., Naralasetti, V.: Composite deep neural network with gated-attention mechanism for diabetic retinopathy severity classification. J. Ambient. Intell. Humaniz. Comput. 12(10), 9825–9839 (2021). https://doi.org/10.1007/s12652-020-02727-zAdriman, R., Muchtar, K., Maulina, N.: Performance evaluation of binary classification of diabetic retinopathy through deep learning techniques using texture feature. Proc. Comput. Sci. 179, 88–94 (2021). https://doi.org/10.1016/j.procs.2020.12.012Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, pp. 2261–2269. Institute of Electrical and Electronics Engineers Inc. (2017). https://doi.org/10.1109/CVPR.2017.243He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 770–778. IEEE Computer Society (2016). https://doi.org/10.1109/CVPR.2016.90Khalifa, N.E.M., Loey, M., Taha, M.H.N., Mohamed, H.N.E.T.: Deep transfer learning models for medical diabetic retinopathy detection. Acta Inform. Medica. 27, 327–332 (2019). https://doi.org/10.5455/aim.2019.27.327-332VGG16 - Convolutional Network for Classification and DetectionVGG-19 convolutional neural network - MATLAB vgg19Gangwar, A.K., Ravi, V.: Diabetic retinopathy detection using transfer learning and deep learning. Presented at the (2021). https://doi.org/10.1007/978-981-15-5788-0_64Google AI Blog: Improving Inception and Image Classification in TensorFlowGoogle colab is a free cloud notebook environment. https://bcrf.biochem.wisc.edu/2021/02/05/google-colab-is-a-free-cloud-notebook-environment/#:~:text=Google. Colab is a free cloud-based service that allows, and install new python libraries. &text=Colab is heavily used for, a platform to learn PythonAPTOS 2019 Blindness Detection | KaggleAPTOS: Eye Preprocessing in Diabetic Retinopathy | KaggleTorres, J.: Deep learning, introducción práctica con Keras (SEGUNDA PARTE) - Jordi TORRES.AIKeras: The Python deep learning API. https://keras.io/Montereal, Q.: APTOS 2019: DenseNet Keras Starter | KaggleKassani, S.H., Kassani, P.H., Khazaeinezhad, R., Wesolowski, M.J., Schneider, K.A., Deters, R.: Diabetic retinopathy classification using a modified xception architecture. In: 2019 IEEE 19th International Symposium on Signal Processing and Information, ISSPIT 2019, pp. 1–6 (2019). https://doi.org/10.1109/ISSPIT47144.2019.9001846Cuello Navarro, J., Barraza Peña, C., Escorcia-Gutiérrez, J.: Una revisión de los métodos de deep learning aplicados a la detección automatizada de la retinopatía diabética. Rev. SEXTANTE 23, 14–33 (2020)Bodapati, J.D., Shaik, N.S., Naralasetti, V.: Deep convolution feature aggregation: an application to diabetic retinopathy severity level prediction. Signal Image Video Process. 15(5), 923–930 (2021). https://doi.org/10.1007/s11760-020-01816-yDekhil, O., Naglah, A., Shaban, M., Ghazal, M., Taher, F., Elbaz, A.: Deep learning based method for computer aided diagnosis of diabetic retinopathy. In: IST 2019 - IEEE International Conference on Imaging Systems and Techniques Proceedings, pp. 19–22 (2019). https://doi.org/10.1109/IST48021.2019.9010333213202© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AGAtribución-NoComercial-CompartirIgual 4.0 Internacional (CC BY-NC-SA 4.0)https://creativecommons.org/licenses/by-nc-sa/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Analysis of pre-trained convolutional neural network models in diabetic retinopathy detection through retinal fundus imagesCapítulo - Parte de Librohttp://purl.org/coar/resource_type/c_3248Textinfo:eu-repo/semantics/bookParthttp://purl.org/redcol/resource_type/CAP_LIBinfo:eu-repo/semantics/drafthttp://purl.org/coar/version/c_b1a7d7d4d402bccehttps://link.springer.com/chapter/10.1007/978-3-031-10539-5_15Diabetic retinopathyRetinal imagingImage recognitionConvolutional neural networkTransfer learningPublicationORIGINALAnalysis of Pre-trained Convolutional Neural Network Models in Diabetic Retinopathy Detection Through Retinal Fundus Images.pdfAnalysis of Pre-trained Convolutional Neural Network Models in Diabetic Retinopathy Detection Through Retinal Fundus Images.pdfapplication/pdf74324https://repositorio.cuc.edu.co/bitstreams/cbefc2eb-bca4-486e-af44-338efa443d72/download6f213f0c14aecc028066e997c682446bMD51LICENSElicense.txtlicense.txttext/plain; charset=utf-83196https://repositorio.cuc.edu.co/bitstreams/452e7598-3207-487f-b3ed-b367f5242a1b/downloade30e9215131d99561d40d6b0abbe9badMD52TEXTAnalysis of Pre-trained Convolutional Neural Network Models in Diabetic Retinopathy Detection Through Retinal Fundus Images.pdf.txtAnalysis of Pre-trained Convolutional Neural Network Models in Diabetic Retinopathy Detection Through Retinal Fundus Images.pdf.txttext/plain1607https://repositorio.cuc.edu.co/bitstreams/ffb08389-26d0-459b-b7a5-7c5b51c78bf3/download6f7948a4cdeba1b4abe6313d5d88b9f8MD53THUMBNAILAnalysis of Pre-trained Convolutional Neural 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