An Ensemble Based Machine Learning Classification for Automated Glaucoma Detection
Abstract Glaucoma is an irredeemable eye disease that causes sight degeneration and is the fourth leading cause of vision impairment as per the World Report on Vision 2019. Several techniques exist for the screening, detection, treatment, and rehabilitation of glaucoma. But still, they are not sufficient to have control over this disease to prevent further vision loss. Studies done on the prevalence of glaucoma have reported a high proportion of undiagnosed patients. Late diagnosis is related to an increased risk of glaucoma associated with visual disability. For the effective management or prevention of blindness, the importance of early diagnosis of glaucoma cannot be underestimated. This paper has proposed an approach for effectively extracting the key features of colour retinal fundus images and categorizing them as normal or glaucomatous. The novel approach of an ensemble machine learning technique has been implemented with an Automated Weightage Based Voting (AWBV) algorithm. This paper has been designed to evaluate the performance of Probabilistic Neural Networks (PNN), K-Nearest Neighbour (KNN), Support Vector Machines (SVM), Naïve Bayes (NB) and Logistic Regression (LR) as individual and ensemble classifiers. It includes the extraction of fused features from various retinal fundus image datasets. The proposed Combined Features Fused Classifier (CF2C) model has had a remarkable performance with the IEEE DataPort image dataset, achieving an ensembled prediction accuracy of 96.25 %, a sensitivity of 95.83 % and a specificity of 96.67 % which are better results than those of the five classifiers individually.
- Referencias
- Cómo citar
- Del mismo autor
- Métricas
Abdar, M., Pourpanah, F., Hussain, S., Rezazadegan, D., Liu, L., Ghavamzadeh, M., Fieguth, P., Cao, X., Khosravi, A., Acharya, U. R., Makarenkov, V., & Nahavandi, S. (2021). A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challenges. Information Fusion, 76, 243-297. ISSN 1566-2535.
Agarwal, A., Gulia, S., Chaudhary, S., Malay, K. D., Carlos, M. T., & Jesús, B. A. (2015). A Novel Approach to Detect Glaucoma in Retinal Fundus Images Using Cup-Disk and Rim-Disk Ratio. In 4th International Work Conference on Bio-inspired Intelligence (IWOBI), 139-144.
Ahuja, A. S. (2019). The Impact of Artificial Intelligence in Medicine on the Future Role of the Physician. Peer J, 7, e7702. 10.7717/peerj.7702
Joshua, A. O., Mabuza-Hocquet, G., & Nelwamondo, F. V. (2020). Assessment of the Cup-To-Disc Ratio Method for Glaucoma Detection. In 2020 IEEE International SAUPEC/RobMech/PRASA Conference, 1-5.
Juliansyah, A., & Nugraha, G. S. (2019). Segmentation of Neuro Retinal Rim Area using Histogram Feature-based for Glaucoma Detection in Retinal Fundus Image. In IEEE International Conference on Advanced Computer Science and Information Systems, 119-124.
Kaggle Dataset. (2022). Glaucoma Detection. Retrieved from https://www.kaggle.com/datasets/sshikamaru/glaucoma-detection.
Kolb, H. (2003). How the Retina Works. American Scientist, Sigma Xi, The Scientific Research Society, 91. Law Kumar Singh, H., Garg, H., & Pooja. (2020). Automated Glaucoma Type Identification Using Machine Learning or Deep Learning Techniques. In O. P. Verma et al. (Eds.), Advancement of Machine Intelligence in Interactive Medical Image Analysis, Algorithms for Intelligent Systems. Springer Nature Singapore Pte. Ltd., 10.1007/978-981-15-1100-4_12
Kostanyan, T., & Wollstein, G. (2015). Ophthalmic Imaging Tools to Detect Glaucoma Progression. Journal-Ophthalmic Imaging Tools to Detect Glaucoma Progression. 8(1), 37–42. 10.17925/usor.2015.8.1.37
Manassakorn, A., Khamwan, K., Owasirikul, D., Itthipanichpong, R., Sa-Ing, V., & Aue-thavekiat, S. (2021). Retinal Nerve Fiber Layer Defect Detection Using Machine Learning on Optic Disc Photograph. In 2021 IEEE EMBS International Conference on Biomedical and Health Informatics (BHI), 1-4.
Salam, A. A., Akram, M. U., Wazir, K., Anwar, S. M., & Majid, M. (2015). Autonomous Glaucoma Detection from Fundus Image Using Cup to Disc Ratio and Hybrid Features. In 2015 IEEE International Symposium on Signal Processing and Information Technology, 370-374.
Salvi, M., Acharya, U. R., Molinari, F., & Meiburger, K. M. (2021). The Impact of Pre- and Post-Image Processing Techniques on Deep Learning Frameworks: A Comprehensive Review for Digital Pathology Image Analysis. Computers in Biology and Medicine, 128, 104-129. 10.1016/j.compbiomed.2021.104129
Sarhan, A., Rokne, J., & Alhajj, R. (2019). Glaucoma Detection Using Image Processing Techniques: A Literature Review. Computerized Medical Imaging and Graphics, 78, 101657. 10.1016/j.compmedimag.2019.101657
Saxena, R., Singh, D., & Vashist, P. (2013). Glaucoma: An Emerging Peril. Indian Journal of Community Medicine, 38(3).
Saxena, A., Vyas, A., Parashar, L., & Singh, U. (2020). A Glaucoma Detection Using Convolutional Neural Network. In 2020 International Conference on Electronics and Sustainable Communication Systems, 815-820.
Sivaswamy, J., Krishnadas, S. R., Joshi, G. D., Ujjwal, M. J., & Tabish, S. (2014). Drishti-GS: Retinal Image Dataset for Optic Nerve Head (ONH) Segmentation. In 2014 IEEE 11th International Symposium on Biomedical Imaging, 53–56. 10.1109/isbi.2014.6867807
Song, W. (2020). 1450 fundus images with 899 glaucoma data and 551 normal data. IEEE DataPort.
Song, W. T., Lai, C., & Su, Y. Z. (2021). A Statistical Robust Glaucoma Detection Framework Combining RetiNex, CNN, and DOE Using Fundus Images. IEEE Access, 9, 103772-103783.
Taj, I. A., Sajid, M., & Karimov, K. S. (2021). An Ensemble Framework Based on Deep CNNs Architecture for Glaucoma Classification using Fundus Photography. Mathematical Bio-Sciences and Engineering, 18(5), 5321-5347.
World Health Organization. (2019). World report on vision. World Health Organization. License: CC BY-NC-SA 3.0 IGO. https://apps.who.int/iris/handle/10665/328717.
Agarwal, A., Gulia, S., Chaudhary, S., Malay, K. D., Carlos, M. T., & Jesús, B. A. (2015). A Novel Approach to Detect Glaucoma in Retinal Fundus Images Using Cup-Disk and Rim-Disk Ratio. In 4th International Work Conference on Bio-inspired Intelligence (IWOBI), 139-144.
Ahuja, A. S. (2019). The Impact of Artificial Intelligence in Medicine on the Future Role of the Physician. Peer J, 7, e7702. 10.7717/peerj.7702
Joshua, A. O., Mabuza-Hocquet, G., & Nelwamondo, F. V. (2020). Assessment of the Cup-To-Disc Ratio Method for Glaucoma Detection. In 2020 IEEE International SAUPEC/RobMech/PRASA Conference, 1-5.
Juliansyah, A., & Nugraha, G. S. (2019). Segmentation of Neuro Retinal Rim Area using Histogram Feature-based for Glaucoma Detection in Retinal Fundus Image. In IEEE International Conference on Advanced Computer Science and Information Systems, 119-124.
Kaggle Dataset. (2022). Glaucoma Detection. Retrieved from https://www.kaggle.com/datasets/sshikamaru/glaucoma-detection.
Kolb, H. (2003). How the Retina Works. American Scientist, Sigma Xi, The Scientific Research Society, 91. Law Kumar Singh, H., Garg, H., & Pooja. (2020). Automated Glaucoma Type Identification Using Machine Learning or Deep Learning Techniques. In O. P. Verma et al. (Eds.), Advancement of Machine Intelligence in Interactive Medical Image Analysis, Algorithms for Intelligent Systems. Springer Nature Singapore Pte. Ltd., 10.1007/978-981-15-1100-4_12
Kostanyan, T., & Wollstein, G. (2015). Ophthalmic Imaging Tools to Detect Glaucoma Progression. Journal-Ophthalmic Imaging Tools to Detect Glaucoma Progression. 8(1), 37–42. 10.17925/usor.2015.8.1.37
Manassakorn, A., Khamwan, K., Owasirikul, D., Itthipanichpong, R., Sa-Ing, V., & Aue-thavekiat, S. (2021). Retinal Nerve Fiber Layer Defect Detection Using Machine Learning on Optic Disc Photograph. In 2021 IEEE EMBS International Conference on Biomedical and Health Informatics (BHI), 1-4.
Salam, A. A., Akram, M. U., Wazir, K., Anwar, S. M., & Majid, M. (2015). Autonomous Glaucoma Detection from Fundus Image Using Cup to Disc Ratio and Hybrid Features. In 2015 IEEE International Symposium on Signal Processing and Information Technology, 370-374.
Salvi, M., Acharya, U. R., Molinari, F., & Meiburger, K. M. (2021). The Impact of Pre- and Post-Image Processing Techniques on Deep Learning Frameworks: A Comprehensive Review for Digital Pathology Image Analysis. Computers in Biology and Medicine, 128, 104-129. 10.1016/j.compbiomed.2021.104129
Sarhan, A., Rokne, J., & Alhajj, R. (2019). Glaucoma Detection Using Image Processing Techniques: A Literature Review. Computerized Medical Imaging and Graphics, 78, 101657. 10.1016/j.compmedimag.2019.101657
Saxena, R., Singh, D., & Vashist, P. (2013). Glaucoma: An Emerging Peril. Indian Journal of Community Medicine, 38(3).
Saxena, A., Vyas, A., Parashar, L., & Singh, U. (2020). A Glaucoma Detection Using Convolutional Neural Network. In 2020 International Conference on Electronics and Sustainable Communication Systems, 815-820.
Sivaswamy, J., Krishnadas, S. R., Joshi, G. D., Ujjwal, M. J., & Tabish, S. (2014). Drishti-GS: Retinal Image Dataset for Optic Nerve Head (ONH) Segmentation. In 2014 IEEE 11th International Symposium on Biomedical Imaging, 53–56. 10.1109/isbi.2014.6867807
Song, W. (2020). 1450 fundus images with 899 glaucoma data and 551 normal data. IEEE DataPort.
Song, W. T., Lai, C., & Su, Y. Z. (2021). A Statistical Robust Glaucoma Detection Framework Combining RetiNex, CNN, and DOE Using Fundus Images. IEEE Access, 9, 103772-103783.
Taj, I. A., Sajid, M., & Karimov, K. S. (2021). An Ensemble Framework Based on Deep CNNs Architecture for Glaucoma Classification using Fundus Photography. Mathematical Bio-Sciences and Engineering, 18(5), 5321-5347.
World Health Organization. (2019). World report on vision. World Health Organization. License: CC BY-NC-SA 3.0 IGO. https://apps.who.int/iris/handle/10665/328717.
Pawar, D. J., Kanse, Y. K., & Patil, S. S. (2024). An Ensemble Based Machine Learning Classification for Automated Glaucoma Detection. ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 13(1), e31640. https://doi.org/10.14201/adcaij.31640
Downloads
Download data is not yet available.
+
−