Analysis of diabetic retinopathy diagnosis using learning based algorithm

G. Renith; A. Senthilselvi

doi:10.53730/ijhs.v6nS3.5218

Authors

Renith G.
rg0453@srmist.edu.in
Research Scholar, Department of CSE, SRM Institute of Science and Technology, Ramapuram, Chennai, India
A. Senthilselvi Associate Professor, Department of CSE, SRM Institute of Science and Technology, Ramapuram, Chennai, India

Keywords:

diabetic retinopathy, deep learning, neural network

Abstract

Diabetic Retinopathy is one of the most dangerous disease and should be identified and treated properly at the very early stage. This is usually diagnosed by scanning the interior structure of human eye with modality like optical coherence tomography and color fundus photography. Then the disease is been diagnosed manually by the respective experts which is a time-consuming process. This process should be automated so that the disease can be diagnosed in a faster and efficient way to reduce the human error. More number of researchers have been done based on automating the diagnosing of diabetic retinopathy disease using machine learning and deep learning approach. The most recent and robust techniques are been discussed in this paper.

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References

Wang, S., Wang, X., Hu, Y., Shen, Y., Yang, Z., Gan, M., & Lei, B. (2020). Diabetic Retinopathy Diagnosis Using Multichannel Generative Adversarial Network With Semisupervision. IEEE Transactions on Automation Science and Engineering, 1–12. doi:10.1109/tase.2020.2981637

Elswah, D. K., Elnakib, A. A., & El-din Moustafa, H. (2020). Automated Diabetic Retinopathy Grading using Resnet. 2020 37th National Radio Science Conference (NRSC). doi:10.1109/nrsc49500.2020.9235

Yalcin, N., Alver, S., & Uluhatun, N. (2018). Classification of retinal images with deep learning for early detection of diabetic retinopathy disease. 2018 26th Signal Processing and Communications Applications Conference (SIU). doi:10.1109/siu.2018.8404369

Jiang, H., Xu, J., Shi, R., Yang, K., Zhang, D., Gao, M., … Qian, W. (2020). A Multi-Label Deep Learning Model with Interpretable Grad-CAM for Diabetic Retinopathy Classification. 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). doi:10.1109/embc44109.2020.917

Patel, R., & Chaware, A. (2020). Transfer Learning with Fine-Tuned MobileNetV2 for Diabetic Retinopathy. 2020 International Conference for Emerging Technology (INCET). doi:10.1109/incet49848.2020.91

Jiang, H., Yang, K., Gao, M., Zhang, D., Ma, H., & Qian, W. (2019). An Interpretable Ensemble Deep Learning Model for Diabetic Retinopathy Disease Classification. 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). doi:10.1109/embc.2019.8857160

Thota, N. B., & Umma Reddy, D. (2020). Improving the Accuracy of Diabetic Retinopathy Severity Classification with Transfer Learning. 2020 IEEE 63rd International Midwest Symposium on Circuits and Systems (MWSCAS). doi:10.1109/mwscas48704.2020.9

Shankar, K., Zhang, Y., Liu, Y., Wu, L., & Chen, C.-H. (2020). Hyperparameter Tuning Deep Learning for Diabetic Retinopathy Fundus Image Classification. IEEE Access, 1–1. doi:10.1109/access.2020.3005152

Chetoui, M., & Akhloufi, M. A. (2020). Explainable Diabetic Retinopathy using EfficientNET*. 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). doi:10.1109/embc44109.2020.9175664

Lands, A., Kottarathil, A. J., Biju, A., Jacob, E. M., & Thomas, S. (2020). Implementation of deep learning based algorithms for diabetic retinopathy classification from fundus images. 2020 4th International Conference on Trends in Electronics and Informatics (ICOEI)(48184). doi:10.1109/icoei48184.2020.9142878

Dharmana, M. M., & M S, A. (2020). Pre-diagnosis of Diabetic Retinopathy using Blob Detection. 2020 Second International Conference on Inventive Research in Computing Applications (ICIRCA). doi:10.1109/icirca48905.2020.9183241

Reddy, G. T., Bhattacharya, S., Siva Ramakrishnan, S., Chowdhary, C. L., Hakak, S., Kaluri, R., & Praveen Kumar Reddy, M. (2020). An Ensemble based Machine Learning model for Diabetic Retinopathy Classification. 2020 International Conference on Emerging Trends in Information Technology and Engineering (ic-ETITE). doi:10.1109/ic-etite47903.2020.235

Liu, Z.; Wang, C.; Cai, X.; Jiang, H.; Wang, J. Discrimination of Diabetic Retinopathy from Optical Coherence Tomography Angiography Images Using Machine Learning Methods. IEEE Access. 2021, 9, 51689–94

Narayanan, B.N.; Hardie, R.C.; De Silva, M.S.; Kueterman, N.K. Hybrid machine learning architecture for automated detection and grading of retinal images for diabetic retinopathy. J Med Imaging. 2020,7, p 034501

Huda, S. M. A., Ila, I. J., Sarder, S., Shamsujjoha, M., & Ali, M. N. Y. (2019). An Improved Approach for Detection of Diabetic Retinopathy Using Feature Importance and Machine Learning Algorithms. 2019 7th International Conference on Smart Computing & Communications (ICSCC). doi:10.1109/icscc.2019.8843676

Shintu Mariam., Arun. (2021). A Deep Learning Based Approach for Automated Diabetic Retinopathy Detection and Grading. 2021 2021 4th Biennial International Conference on Nascent Technologies in Engineering (ICNTE). doi:10.1109/ICNTE51185.2021.9487759

Deepa, Sathish Kumar, Thomas Cherian (2021). Automated Detection of Diabetic Retinopathy Images using Pre-trained Convolutional Neural Network. 2021 International Conference on Communication, Control and Information Sciences (ICCISc). DOI: 10.1109/ICCISc52257.2021.9484967

Ramya Bygari, Rachita Naik, Uday Kumar (2021). Blindness (Diabetic Retinopathy) Severity Scale Detection. 2021 8th Swiss Conference on Data Science (SDS) DOI: 10.1109/SDS51136.2021.00009.

Yash S. Boral, Snehal S. Thorat (2021). Classification of Diabetic Retinopathy based on Hybrid Neural Network. 2021 5th International Conference on Computing Methodologies and Communication (ICCMC). DOI: 10.1109/ICCMC51019.2021.9418224.

Anik Ghosh, Aowlad Hossain (2021). Classification of Diabetic Retinopathy Using Few-Shot Transfer Learning from Imbalanced Data. 2021 7th International Conference on Advanced Computing and Communication Systems (ICACCS). DOI: 10.1109/ICACCS51430.2021.9442024.

Harshit Kaushik, Dilbag singh, Manjit kaur, Hammam, Atef, Habib hamam (2021). Diabetic Retinopathy Diagnosis From Fundus Images Using Stacked Generalization of Deep Models. DOI: 10.1109/ACCESS.2021.3101142.

Saeed, F.; Hussain, M.; Aboalsamh, H.A. Automatic Diabetic Retinopathy Diagnosis Using Adaptive Fine-Tuned Convolutional Neural Network. IEEE Access. 2021, 9, 41344–59.

He, A.; Li, T.; Li, N.; Wang, K.; Fu, H. CABNet: Category Attention Block for Imbalanced Diabetic Retinopathy Grading. IEEE Trans Med Imaging. 2021, 40, 143–53.

Abdelmaksoud, E.; El-Sappagh, S.; Barakat, S.; Abuhmed, T.; Elmogy, M. Automatic Diabetic Retinopathy Grading System Based on Detecting Multiple Retinal Lesions. IEEE Access. 2021, 9, 15939–60.

Hsieh, YT.; Chuang, LM.; Jiang, Y Der.; Chang, TJ.; Yang, CM, et al. Application of deep learning image assessment software VeriSeeTM for diabetic retinopathy screening. J Formos Med Assoc. 2021, 120, 165–71. DOI:10.1016/j.jfma.2020.03.024

Shaban, M.; Ogur, Z.; Mahmoud, A.; Switala, A.; Shalaby, A, et al. A convolutional neural network for the screening and staging of diabetic retinopathy. PLoS One. 2020, 15, 1–13; DOI: 10.1371/journal.pone.0233514.

Pao, S.I.; Lin, H.Z.; Chien, K.H.; Tai, M.C.; Chen, J.T.; Lin, G.M. Detection of Diabetic Retinopathy Using Bichannel Convolutional Neural Network. J Ophthalmol. 2020. DOI: 10.1155/2020/9139713.

[G. Renith, A Senthilselvi , “Accuracy Improvement in Diabetic Retinopathy Detection using DLIA”, Journal of Advanced Research in Dynamical and Control Systems, titled Volume 12, Issue 7, July 2020

V. Surya, A Senthilselvi , “A Qualitative Analysis of the Machine Learning Methods in Food Adultery: A Focus on Milk Adulteration Detection”, Journal of Advanced Research in Dynamical and Control Systems, titled Volume 12, Issue 4, May 2020

A. Senthilselvi , V. Sellam , Saad Ali Alahmari , Sivaram Rajeyyagari ,” Accuracy enhancement in mobile phone recycling process using machine learning technique and MEPH process”, Environmental Technology & Innovation,volume20,November2020

Senthilselvi, A., Shiny Duela, J.S., Prabavathi, R. et al. Performance evaluation of adaptive neuro fuzzy system (ANFIS) over fuzzy inference system (FIS) with optimization algorithm in de-noising of images from salt and pepper noise. J Ambient Intell Human Comput (2021). https://doi.org/10.1007/s12652-021-03024-z

A. Senthilselvi, Sweta Pasayat , Kirthika Sivakumar , M Reene Stephanie “An Adaptive, Dynamic and Semantic Approach for Understanding of Sign Language based on Convolution Neural Network”., Turkish Online Journal of Qualitative Inquiry (TOJQI), Volume 12, Issue 5, June 2021: 1331– 1344.

Sankareswaran Pandi Senthil *, Krishnan Mahadevan , Unsupervised End-to-End Brain Tumor Magnetic Resonance Image Registration Using RBCNN: Rigid Transformation, B-Spline Transformation and Convolutional Neural Network, Current Medical Imaging 2022; 18(4) . https://dx.doi.org/10.2174/1573405617666210806125526Surya, V., [34] Senthilselvi, A. Identification of oil authenticity and adulteration using deep long short-term memory-based neural network with seagull optimization algorithm. Neural Comput & Applic (2022). https://doi.org/10.1007/s00521-021-06829-3