Glaucoma Disease Classification by using Fundus images
DOI:
https://doi.org/10.61506/01.00263Keywords:
Glacoma Disease Detection, Glaucoma disease classification, Glaucoma diseaseAbstract
Optic Disc (OD) and Optic Cup (OC) damage is caused by the eye condition glaucoma. OD is the morphological structure that is apparent in the cross-sectional view of the optic nerve connecting to the retina, while OC is the core region of OD. The morphological changes in the optic disc (OD) and optic cup (OC) often happen before visual field issues when glaucoma begins. Optic nerve head damage caused by glaucoma is permanent. Glaucoma is the greatest global cause of irreversible blindness, according to data from the World Health Organization (WHO). Only 10 to 50 percent of glaucoma patients, according to population-level surveys, are aware that they have the condition. As a result, glaucoma early identification is crucial for preventing irreversible eye damage. Glaucoma is a vision disorder that frequently affects older people and renders them permanently blind. Glaucoma affects 2.5% of people of all ages and 4.8% of people over the age of 75. Using MobileNetV2, this study suggests a unique deep transfer learning model for categorizing glaucoma. With regard to the error, with the least amount of expense, MobileNetV2 is a framework that optimizes memory consumption and execution speed. To increase the dataset and MobileNetV2's precision, data augmentation techniques were used. Using the HRF dataset, the suggested deep learning model's effectiveness is assessed. Results from the suggested procedure are accurate to 98%. Medical professionals can find the optimum course of treatment for their patients with the help of automated glaucoma classification.
References
Ajitha, S., & Judy, M. (2020). Faster R-CNN classification for the recognition of glaucoma. Journal of Physics: Conference Series, DOI: https://doi.org/10.1088/1742-6596/1706/1/012170
Alghamdi, M., & Abdel-Mottaleb, M. (2021). A comparative study of deep learning models for diagnosing glaucoma from fundus images. IEEE Access, 9, 23894-23906. DOI: https://doi.org/10.1109/ACCESS.2021.3056641
Ansari, E., Pavicic-Astalos, J., Ayan, F., King, A. J., Kinsella, M., Ng, E., Nita, A., & Group, V. (2021). Treatment of open-angle glaucoma and ocular hypertension with preservative-free tafluprost/timolol fixed-dose combination therapy: UK and Ireland results from the VISIONARY Study. Advances in therapy, 38(6), 2990-3002. DOI: https://doi.org/10.1007/s12325-021-01725-7
Arbeiter, D., Reske, T., Teske, M., Bajer, D., Senz, V., Schmitz, K.-P., Grabow, N., & Oschatz, S. (2021). Influence of drug incorporation on the physico-chemical properties of poly (L-lactide) implant coating matrices—A systematic study. Polymers, 13(2), 292. DOI: https://doi.org/10.3390/polym13020292
Camara, J., Neto, A., Pires, I. M., Villasana, M. V., Zdravevski, E., & Cunha, A. (2022). Literature review on artificial intelligence methods for glaucoma screening, segmentation, and classification. Journal of Imaging, 8(2), 19. DOI: https://doi.org/10.3390/jimaging8020019
Cvenkel, B., & Kolko, M. (2020). Current medical therapy and future trends in the management of glaucoma treatment. Journal of Ophthalmology, 2020. DOI: https://doi.org/10.1155/2020/6138132
Devecioglu, O. C., Malik, J., Ince, T., Kiranyaz, S., Atalay, E., & Gabbouj, M. (2021). Real-time glaucoma detection from digital fundus images using self-onns. IEEE Access, 9, 140031-140041. DOI: https://doi.org/10.1109/ACCESS.2021.3118102
Fu’adah, Y. N., Sa’idah, S., Wijayanto, I., Ibrahim, N., Rizal, S., & Magdalena, R. (2021). Computer Aided Diagnosis for Early Detection of Glaucoma Using Convolutional Neural Network (CNN). Proceedings of the 1st International Conference on Electronics, Biomedical Engineering, and Health Informatics: ICEBEHI 2020, 8-9 October, Surabaya, Indonesia, DOI: https://doi.org/10.1007/978-981-33-6926-9_40
Gharahkhani, P., Jorgenson, E., Hysi, P., Khawaja, A. P., Pendergrass, S., Han, X., Ong, J. S., Hewitt, A. W., Segre, A., & Igo Jr, R. P. (2020). A large cross-ancestry meta-analysis of genome-wide association studies identifies 69 novel risk loci for primary open-angle glaucoma and includes a genetic link with Alzheimer’s disease. BioRxiv, 2020.2001. 2030.927822. DOI: https://doi.org/10.1101/2020.01.30.927822
Guo, F., Li, W., Tang, J., Zou, B., & Fan, Z. (2020). Automated glaucoma screening method based on image segmentation and feature extraction. Medical & Biological Engineering & Computing, 58, 2567-2586. DOI: https://doi.org/10.1007/s11517-020-02237-2
Hervella, Á. S., Rouco, J., Novo, J., & Ortega, M. (2022). End-to-end multi-task learning for simultaneous optic disc and cup segmentation and glaucoma classification in eye fundus images. Applied Soft Computing, 116, 108347. DOI: https://doi.org/10.1016/j.asoc.2021.108347
Islam, M. T., Mashfu, S. T., Faisal, A., Siam, S. C., Naheen, I. T., & Khan, R. (2021). Deep learning-based glaucoma detection with cropped optic cup and disc and blood vessel segmentation. IEEE Access, 10, 2828-2841. DOI: https://doi.org/10.1109/ACCESS.2021.3139160
Kanse, S. S., & Yadav, D. M. (2019). Retinal fundus image for glaucoma detection: A review and study. Journal of Intelligent Systems, 28(1), 43-56. DOI: https://doi.org/10.1515/jisys-2016-0258
Khan, S. I., Choubey, S. B., Choubey, A., Bhatt, A., Naishadhkumar, P. V., & Basha, M. M. (2022). Automated glaucoma detection from fundus images using wavelet-based denoising and machine learning. Concurrent Engineering, 30(1), 103-115. DOI: https://doi.org/10.1177/1063293X211026620
Khatib, T. Z., & Martin, K. R. (2020). Neuroprotection in glaucoma: towards clinical trials and precision medicine. Current Eye Research, 45(3), 327-338. DOI: https://doi.org/10.1080/02713683.2019.1663385
Kirar, B. S., Reddy, G. R. S., & Agrawal, D. K. (2021). Glaucoma Detection Using SS-QB-VMD-Based Fine Sub-Band Images from Fundus Images. IETE Journal of Research, 1-12. DOI: https://doi.org/10.1080/03772063.2021.1959424
Latif, J., Tu, S., Xiao, C., Ur Rehman, S., Imran, A., & Latif, Y. (2022). ODGNet: a deep learning model for automated optic disc localization and glaucoma classification using fundus images. SN Applied Sciences, 4(4), 98. DOI: https://doi.org/10.1007/s42452-022-04984-3
Nawaz, M., Nazir, T., Javed, A., Tariq, U., Yong, H.-S., Khan, M. A., & Cha, J. (2022). An efficient deep learning approach to automatic glaucoma detection using optic disc and optic cup localization. Sensors, 22(2), 434. DOI: https://doi.org/10.3390/s22020434
Nayak, D. R., Das, D., Majhi, B., Bhandary, S. V., & Acharya, U. R. (2021). ECNet: An evolutionary convolutional network for automated glaucoma detection using fundus images. Biomedical Signal Processing and Control, 67, 102559. DOI: https://doi.org/10.1016/j.bspc.2021.102559
Neto, A., Camara, J., & Cunha, A. (2022). Evaluations of deep learning approaches for glaucoma screening using retinal images from mobile device. Sensors, 22(4), 1449. DOI: https://doi.org/10.3390/s22041449
Nguyen, T. X., Ran, A. R., Hu, X., Yang, D., Jiang, M., Dou, Q., & Cheung, C. Y. (2022). Federated learning in ocular imaging: current progress and future direction. Diagnostics, 12(11), 2835. DOI: https://doi.org/10.3390/diagnostics12112835
O'bryhim, B. E., Samara, A., Chen, L., Hershey, T., Tychsen, L., & Hoekel, J. (2022). Longitudinal Changes in Vision and Retinal Morphology in Wolfram Syndrome. American journal of ophthalmology, 243, 10-18. DOI: https://doi.org/10.1016/j.ajo.2022.07.003
Passerini, N., & Lombardi, C. (2020). Postponing the Concept of Class When Introducing OOP. Proceedings of the 2020 ACM Conference on Innovation and Technology in Computer Science Education, DOI: https://doi.org/10.1145/3341525.3387369
Perkins, H. (2017). CUDA-on-CL: a compiler and runtime for running NVIDIA® CUDA™ C++ 11 applications on OpenCL™ 1.2 Devices. Proceedings of the 5th International Workshop on OpenCL, DOI: https://doi.org/10.1145/3078155.3078156
Ramesh, P. V., Parthasarathi, S., & John, R. K. (2021). An exploratory study of compliance to anti-glaucoma medications among literate primary glaucoma patients at an urban tertiary eye care center in South India. Indian journal of ophthalmology, 69(6), 1418. DOI: https://doi.org/10.4103/ijo.IJO_2008_20
Robbins, C. C., Anjum, S., Alwreikat, A. M., Cooper, M. L., Cotran, P. R., Roh, S., & Ramsey, D. J. (2021). An initiative to improve follow-up of patients with glaucoma. Ophthalmology Science, 1(4), 100059. DOI: https://doi.org/10.1016/j.xops.2021.100059
Saxena, A., Vyas, A., Parashar, L., & Singh, U. (2020). A glaucoma detection using convolutional neural network. 2020 international conference on electronics and sustainable communication systems (ICESC), DOI: https://doi.org/10.1109/ICESC48915.2020.9155930
Serte, S., & Serener, A. (2021). Graph‐based saliency and ensembles of convolutional neural networks for glaucoma detection. IET Image Processing, 15(3), 797-804. DOI: https://doi.org/10.1049/ipr2.12063
Shoba, S. G., & Therese, A. B. (2020). Detection of glaucoma disease in fundus images based on morphological operation and finite element method. Biomedical Signal Processing and Control, 62, 101986. DOI: https://doi.org/10.1016/j.bspc.2020.101986
Singh, L. K., Khanna, M., Thawkar, S., & Singh, R. (2023). Nature-inspired computing and machine learning based classification approach for glaucoma in retinal fundus images. Multimedia Tools and Applications, 1-49. DOI: https://doi.org/10.1007/s11042-023-15175-6
Yu, Y., Adu, K., Tashi, N., Anokye, P., Wang, X., & Ayidzoe, M. A. J. I. A. (2020). Rmaf: Relu-memristor-like activation function for deep learning. 8, 72727-72741. DOI: https://doi.org/10.1109/ACCESS.2020.2987829
Zhao, W., Lv, X., Wu, G., Zhou, X., Tian, H., Qu, X., Sun, H., He, Y., Zhang, Y., & Wang, C. (2021). Glaucoma is not associated with Alzheimer's disease or dementia: a meta-analysis of cohort studies. Frontiers in Medicine, 8, 688551. DOI: https://doi.org/10.3389/fmed.2021.688551
