Machine Learning Based Prediction of Retinopathy Diseases Using Segmented Retinal Images
Resumen Diabetes, hypertension, obesity, glaucoma, etc. are severe and common retinopathy diseases today. Early age detection and diagnosis of these diseases can save human beings from many life threats. The retina’s blood vessels carry details of retinopathy diseases. Therefore, feature extraction from blood vessels is essential to classify these diseases. A segmented retinal image is only a vascular tree of blood vessels. Feature extraction is easy and efficient from segmented images. Today, there are existing different approaches in this field that use RGB images only to classify these diseases due to which their performance is relatively low. In the work, we have proposed a model based on machine learning that uses segmented retinal images generated by different efficient methods to classify diabetic retinopathy, glaucoma, and multi-class diseases. We have carried out extensive experiments on numerous images of DRIVE, HRF, STARE, and RIM-ONE DL datasets. The highest accuracy of the proposed approach is 90.90 %, 95.00 %, and 92.90 % for diabetic retinopathy, glaucoma, and multi-class diseases, respectively, which the model detected better than most of the methods in this field.
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Abitbol, E., Miere, A., Excoffier, J. B., Mehanna, C. J., Amoroso, F., Kerr, S., Ortala, M., & Souied, E. H. (2022). Deep learning based classification of retinal vascular diseases using ultra widefield color fundus photographs. BMJ Open Ophthalmology, 7(1), 1-7. https://doi.org/10.1136/bmjophth-2021-000876
Alam, M., Zhao, E. J., Lam, C. K., & Rubin, D. L. (2023). Segmentation-assisted fully convolutional neural network enhances deep learning performance to identify proliferative diabetic retinopathy. Journal of Clinical Medicine, 12(1). https://doi.org/10.3390/jcm12010001
Attia, A., Akhtar, Z., Akhrouf, S., & Maza, S. (2020). A survey on machine and deep learning for detection of diabetic retinopathy. Image and Video Processing, 11(2), 2337-2344. https://doi.org/10.1007/s11042-020-08625-1
Batista, F., & José, F. (2020). A unified retinal image database for assessing glaucoma using deep learning. Image Analysis & Stereology, 39(3), 161-167. https://doi.org/10.5566/ias.2210
Boser, E. B., Guyon, M. I., & Vapnik, N. V. (1992). A training algorithm for optimal margin classifiers. In Proceedings of the fifth annual workshop on Computational Learning Theory-COLT (pp. 144-152). https://doi.org/10.1145/130385.130401
Cao, J., Chen, J., Zhang, X., Yan, Q., & Zhao, Y. (2022). Attentional mechanisms and improved residual networks for diabetic retinopathy severity classification. Journal of Healthcare Engineering, 1-10. https://doi.org/10.1155/2022/1234567
Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273-297. https://doi.org/10.1007/BF00994018
Cover, T. M., & Hart, E. P. (1967). Nearest neighbor pattern classification. IEEE Transactions on Information Theory, 13(1), 21-27. https://doi.org/10.1109/TIT.1967.1053964
Das, A., Giri, R., Chourasia, G., & Bala, A. A. (2019). Classification of retinal diseases using transfer learning approach. In Proceedings of the Fourth International Conference on Communication and Electronics Systems (pp. 2075-2079). Coimbatore, India. https://doi.org/10.1109/ICCES.2019.8924101
Fisher, R. A. (1936). The use of multiple measurements in taxonomic problems. Annals of Eugenics, 7(2), 179-188. https://doi.org/10.1111/j.1469-1809.1936.tb02137.x
Fix, E., & Hodges, J. (1951). Discriminatory analysis. Non-parametric discrimination: Consistency properties. USAF School of Aviation Medicine, Randolph Field, Texas. https://doi.org/10.21236/AD0246377
Gour, N., & Khanna, P. (2021). Multi-class multi-label ophthalmological disease detection using transfer learning based convolutional neural network. Biomedical Signal Processing and Control, 66, 1-8. https://doi.org/10.1016/j.bspc.2021.102492
Han, Y., Li, W., Liu, M., Wu, Z., Zhang, F., Liu, X., Tao, L., Li, X., & Guo, X. (2021). Application of an anomaly detection model to screen for ocular diseases using color retinal fundus images: Design and evaluation study. Journal of Medical Internet Research, 23(7), 1-12. https://doi.org/10.2196/23456
Haralick, R. M., Shanmugam, K., & Dinstein, I. (1973). Textural features for image classification. IEEE Transactions on Systems, Man, and Cybernetics, 3(6), 610-621. https://doi.org/10.1109/TSMC.1973.4309314
Hoover, A., Kouznetsova, V., & Goldbaum, M. (2000). Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response. IEEE Transactions on Medical Imaging, 19(3), 203-210. https://doi.org/10.1109/42.845178
Jiang, H., Yang, K., Gao, M., Zhang, D., Ma, H., & Qian, W. (2019). An interpretable ensemble deep learning model for diabetic retinopathy disease classification. In 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 2045-2048). https://doi.org/10.1109/EMBC.2019.8857745
Juneja, M., Thakur, S., Uniyal, A., Wani, A., Thakur, N., & Jindal, P. (2022). Deep learning-based classification network for glaucoma in retinal images. Computers and Electrical Engineering, 101. https://doi.org/10.1016/j.compeleceng.2022.107995
Korkmaz, S. A., Akçiçek, A., Bínol, H., & Korkmaz, M. F. (2017). Recognition of the stomach cancer images with probabilistic HOG feature vector histograms by using HOG features. In 15th International Symposium on Intelligent Systems and Informatics (SISY) (pp. 000339-000342). https://doi.org/10.1109/SISY.2017.8080563
Laws, K. (1980). Rapid texture identification. Image Processing for Missile Guidance, 238, 376-380. https://doi.org/10.1117/12.959774
Li, T., Gao, Y., Wang, K., Guo, S., Liu, H., & Kang, H. (2019). Diagnostic assessment of deep learning algorithms for diabetic retinopathy screening. Information Sciences, 501, 511-522. https://doi.org/10.1016/j.ins.2019.06.011
Londhe, M. (2021). Classification of eye diseases using hybrid CNN-RNN models. MSc Project, National College of Ireland, 1-20. https://doi.org/10.1109/ICCMC.2021.9763803
Mallat, S. (1989). Multifrequency channel decomposition of images and wavelet models. IEEE Transaction on ASSP, 37(12), 2091-2110. https://doi.org/10.1109/29.45599
McLachlan, G. J. (2004). Discriminant analysis and statistical pattern recognition. Wiley Interscience. ISBN 978-0-471-69115-0. https://doi.org/10.1002/0471725293
Muchuchuti, S., & Viriri, S. (2023). Retinal disease detection using deep learning techniques: A comprehensive review. Journal of Imaging, 9(84), 1-38. https://doi.org/10.3390/jimaging9080084
Nazir, T., Nawaz, M., Rashid, J., Mahum, R., Masood, M., Mehmood, A., Ali, F., Kim, J., Kwon, H.-Y., & Hussain, A. (2021). Detection of diabetic eye disease from retinal images using a deep learning based CenterNet model. Sensors, 21(16), 1-18. https://doi.org/10.3390/s21165528
Odstrcilik, J., Kolar, R., Budai, A., Hornegger, J., Jan, J., Gazarek, J., Kubena, T., Cernosek, P., Svoboda, O., & Angelopoulou, E. (2013). Retinal vessel segmentation by improved match filtering: Evaluation on a new high resolution fundus image database. IET Image Processing, 7(4), 373-383. https://doi.org/10.1049/iet-ipr.2012.0455
Ojala, T., Pietikainen, M., & Maenpaa, T. (2002). Multi-resolution grayscale and rotation invariant texture classification with local binary patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(7), 971-987. https://doi.org/10.1109/TPAMI.2002.1017623
Opitz, D., & Maclin, R. (1999). Popular ensemble methods: An empirical study. Journal of Artificial Intelligence Research, 11, 169-198. https://doi.org/10.1613/jair.614
Pin, K., Chang, J. H., & Nam, Y. (2022). Comparative study of transfer learning models for retinal disease diagnosis from fundus images. Computers, Materials & Continua, 70(3), 5821-5834. https://doi.org/10.32604/cmc.2022.021583
Pinto, A.-D., Morales, S., Naranjo, V., Köhler, T., Mossi, J. M., & Navea, A. (2019). CNNs for automatic glaucoma assessment using fundus images: An extensive validation. BioMedical Engineering OnLine, 18, 29, 1-19. https://doi.org/10.1186/s12938-019-0652-5
Qin, C., & Siang, S. (2016). An efficient method of HOG feature extraction using selective histogram bin and PCA feature reduction. Advances in Electrical and Computer Engineering, 16(4), 101-108. https://doi.org/10.4316/AECE.2016.04013
Quinlan, J. R. (1986). Induction of decision trees. Machine Learning, 1, 81-106. https://doi.org/10.1007/BF00116251
Rokach, L. (2010). Ensemble-based classifiers. Artificial Intelligence Review, 33(1-2), 1-39. https://doi.org/10.1007/s10462-009-9124-7
Rokach, L., & Maimon, O. (2014). Data mining with decision trees: Theory and applications. 2nd Edition, World Scientific Publishing Co Inc, 69, 1-244. https://doi.org/10.1142/9789814590090
Sarki, R., Ahmed, K., Wang, H., & Zhang, Y. (2020). Automated detection of mild and multi-class diabetic eye diseases using deep learning. Health Information Science and Systems, 8(1), 1-9. https://doi.org/10.1007/s13755-020-00111-8
Saroj, S. K., Kumar, R., & Singh, N. P. (2020). Fréchet PDF based matched filter approach for retinal blood vessels segmentation. Computer Methods and Programs in Biomedicine, 194, 1-17. https://doi.org/10.1016/j.cmpb.2020.105515
Saroj, S. K., Kumar, R., & Singh, N. P. (2022). Retinal blood vessels segmentation using Wald PDF and MSMO operator. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 10(2), 1-18. https://doi.org/10.1080/21681163.2021.1989467
Saroj, S. K., Ratna, V., Kumar, R., & Singh, N. P. (2020). Efficient kernel based matched filter approach for segmentation of retinal blood vessels. Solid State Technology, 63(5), 7318-7334. https://doi.org/10.1016/j.sst.2020.05.001
Sengar, N., Joshi, R. C., Dutta, M. K., & Burget, R. (2023). EyeDeep-Net: A multi-class diagnosis of retinal diseases using deep neural network. Neural Computing and Applications, 35, 1-21. https://doi.org/10.1007/s00521-022-07345-1
Shrivastava, A., Kamble, R., Kulkarni, S., Singh, S., Hegde, A., Kashikar, R., & Das, T. (2021). Deep learning based ocular disease classification using retinal fundus images. Investigative Ophthalmology & Visual Science, 63(11). https://doi.org/10.1167/iovs.63.11.1
Staal, J., Abramoff, M. D., Niemeijer, M., Viergever, M. A., & Ginneken, B. V. (2004). Ridge based vessel segmentation in color images of the retina. IEEE Transaction on Medical Imaging, 23(4), 501-509. https://doi.org/10.1109/TMI.2004.825627
Tamura, H., Mori, S., & Yamawaki, T. (1978). Textural features corresponding to visual perception. IEEE Transactions on Systems, Man, and Cybernetics, 8(6), 460-473. https://doi.org/10.1109/TSMC.1978.4309999
Tolles, J., & Meurer, J. W. (2016). Logistic regression relating patient characteristics to outcomes. JAMA, 316(5), 533-534. https://doi.org/10.1001/jama.2016.7653
Virmani, J., Singh, G. P., Singh, Y., & Kriti. (2019). PNN-based classification of retinal diseases using fundus images. Sensors for Health Monitoring, 5, 215-242. https://doi.org/10.3390/s1909215
Zhu, S., Lu, B., Wang, C., Wu, M., Zheng, B., Jiang, Q., Wei, R., Cao, Q., & Yang, W. (2022). Screening of common retinal diseases using six-category models based on EfficientNet. Frontiers in Medicine, 9, 1-9. https://doi.org/10.3389/fmed.2022.1234567
Alam, M., Zhao, E. J., Lam, C. K., & Rubin, D. L. (2023). Segmentation-assisted fully convolutional neural network enhances deep learning performance to identify proliferative diabetic retinopathy. Journal of Clinical Medicine, 12(1). https://doi.org/10.3390/jcm12010001
Attia, A., Akhtar, Z., Akhrouf, S., & Maza, S. (2020). A survey on machine and deep learning for detection of diabetic retinopathy. Image and Video Processing, 11(2), 2337-2344. https://doi.org/10.1007/s11042-020-08625-1
Batista, F., & José, F. (2020). A unified retinal image database for assessing glaucoma using deep learning. Image Analysis & Stereology, 39(3), 161-167. https://doi.org/10.5566/ias.2210
Boser, E. B., Guyon, M. I., & Vapnik, N. V. (1992). A training algorithm for optimal margin classifiers. In Proceedings of the fifth annual workshop on Computational Learning Theory-COLT (pp. 144-152). https://doi.org/10.1145/130385.130401
Cao, J., Chen, J., Zhang, X., Yan, Q., & Zhao, Y. (2022). Attentional mechanisms and improved residual networks for diabetic retinopathy severity classification. Journal of Healthcare Engineering, 1-10. https://doi.org/10.1155/2022/1234567
Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273-297. https://doi.org/10.1007/BF00994018
Cover, T. M., & Hart, E. P. (1967). Nearest neighbor pattern classification. IEEE Transactions on Information Theory, 13(1), 21-27. https://doi.org/10.1109/TIT.1967.1053964
Das, A., Giri, R., Chourasia, G., & Bala, A. A. (2019). Classification of retinal diseases using transfer learning approach. In Proceedings of the Fourth International Conference on Communication and Electronics Systems (pp. 2075-2079). Coimbatore, India. https://doi.org/10.1109/ICCES.2019.8924101
Fisher, R. A. (1936). The use of multiple measurements in taxonomic problems. Annals of Eugenics, 7(2), 179-188. https://doi.org/10.1111/j.1469-1809.1936.tb02137.x
Fix, E., & Hodges, J. (1951). Discriminatory analysis. Non-parametric discrimination: Consistency properties. USAF School of Aviation Medicine, Randolph Field, Texas. https://doi.org/10.21236/AD0246377
Gour, N., & Khanna, P. (2021). Multi-class multi-label ophthalmological disease detection using transfer learning based convolutional neural network. Biomedical Signal Processing and Control, 66, 1-8. https://doi.org/10.1016/j.bspc.2021.102492
Han, Y., Li, W., Liu, M., Wu, Z., Zhang, F., Liu, X., Tao, L., Li, X., & Guo, X. (2021). Application of an anomaly detection model to screen for ocular diseases using color retinal fundus images: Design and evaluation study. Journal of Medical Internet Research, 23(7), 1-12. https://doi.org/10.2196/23456
Haralick, R. M., Shanmugam, K., & Dinstein, I. (1973). Textural features for image classification. IEEE Transactions on Systems, Man, and Cybernetics, 3(6), 610-621. https://doi.org/10.1109/TSMC.1973.4309314
Hoover, A., Kouznetsova, V., & Goldbaum, M. (2000). Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response. IEEE Transactions on Medical Imaging, 19(3), 203-210. https://doi.org/10.1109/42.845178
Jiang, H., Yang, K., Gao, M., Zhang, D., Ma, H., & Qian, W. (2019). An interpretable ensemble deep learning model for diabetic retinopathy disease classification. In 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (pp. 2045-2048). https://doi.org/10.1109/EMBC.2019.8857745
Juneja, M., Thakur, S., Uniyal, A., Wani, A., Thakur, N., & Jindal, P. (2022). Deep learning-based classification network for glaucoma in retinal images. Computers and Electrical Engineering, 101. https://doi.org/10.1016/j.compeleceng.2022.107995
Korkmaz, S. A., Akçiçek, A., Bínol, H., & Korkmaz, M. F. (2017). Recognition of the stomach cancer images with probabilistic HOG feature vector histograms by using HOG features. In 15th International Symposium on Intelligent Systems and Informatics (SISY) (pp. 000339-000342). https://doi.org/10.1109/SISY.2017.8080563
Laws, K. (1980). Rapid texture identification. Image Processing for Missile Guidance, 238, 376-380. https://doi.org/10.1117/12.959774
Li, T., Gao, Y., Wang, K., Guo, S., Liu, H., & Kang, H. (2019). Diagnostic assessment of deep learning algorithms for diabetic retinopathy screening. Information Sciences, 501, 511-522. https://doi.org/10.1016/j.ins.2019.06.011
Londhe, M. (2021). Classification of eye diseases using hybrid CNN-RNN models. MSc Project, National College of Ireland, 1-20. https://doi.org/10.1109/ICCMC.2021.9763803
Mallat, S. (1989). Multifrequency channel decomposition of images and wavelet models. IEEE Transaction on ASSP, 37(12), 2091-2110. https://doi.org/10.1109/29.45599
McLachlan, G. J. (2004). Discriminant analysis and statistical pattern recognition. Wiley Interscience. ISBN 978-0-471-69115-0. https://doi.org/10.1002/0471725293
Muchuchuti, S., & Viriri, S. (2023). Retinal disease detection using deep learning techniques: A comprehensive review. Journal of Imaging, 9(84), 1-38. https://doi.org/10.3390/jimaging9080084
Nazir, T., Nawaz, M., Rashid, J., Mahum, R., Masood, M., Mehmood, A., Ali, F., Kim, J., Kwon, H.-Y., & Hussain, A. (2021). Detection of diabetic eye disease from retinal images using a deep learning based CenterNet model. Sensors, 21(16), 1-18. https://doi.org/10.3390/s21165528
Odstrcilik, J., Kolar, R., Budai, A., Hornegger, J., Jan, J., Gazarek, J., Kubena, T., Cernosek, P., Svoboda, O., & Angelopoulou, E. (2013). Retinal vessel segmentation by improved match filtering: Evaluation on a new high resolution fundus image database. IET Image Processing, 7(4), 373-383. https://doi.org/10.1049/iet-ipr.2012.0455
Ojala, T., Pietikainen, M., & Maenpaa, T. (2002). Multi-resolution grayscale and rotation invariant texture classification with local binary patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(7), 971-987. https://doi.org/10.1109/TPAMI.2002.1017623
Opitz, D., & Maclin, R. (1999). Popular ensemble methods: An empirical study. Journal of Artificial Intelligence Research, 11, 169-198. https://doi.org/10.1613/jair.614
Pin, K., Chang, J. H., & Nam, Y. (2022). Comparative study of transfer learning models for retinal disease diagnosis from fundus images. Computers, Materials & Continua, 70(3), 5821-5834. https://doi.org/10.32604/cmc.2022.021583
Pinto, A.-D., Morales, S., Naranjo, V., Köhler, T., Mossi, J. M., & Navea, A. (2019). CNNs for automatic glaucoma assessment using fundus images: An extensive validation. BioMedical Engineering OnLine, 18, 29, 1-19. https://doi.org/10.1186/s12938-019-0652-5
Qin, C., & Siang, S. (2016). An efficient method of HOG feature extraction using selective histogram bin and PCA feature reduction. Advances in Electrical and Computer Engineering, 16(4), 101-108. https://doi.org/10.4316/AECE.2016.04013
Quinlan, J. R. (1986). Induction of decision trees. Machine Learning, 1, 81-106. https://doi.org/10.1007/BF00116251
Rokach, L. (2010). Ensemble-based classifiers. Artificial Intelligence Review, 33(1-2), 1-39. https://doi.org/10.1007/s10462-009-9124-7
Rokach, L., & Maimon, O. (2014). Data mining with decision trees: Theory and applications. 2nd Edition, World Scientific Publishing Co Inc, 69, 1-244. https://doi.org/10.1142/9789814590090
Sarki, R., Ahmed, K., Wang, H., & Zhang, Y. (2020). Automated detection of mild and multi-class diabetic eye diseases using deep learning. Health Information Science and Systems, 8(1), 1-9. https://doi.org/10.1007/s13755-020-00111-8
Saroj, S. K., Kumar, R., & Singh, N. P. (2020). Fréchet PDF based matched filter approach for retinal blood vessels segmentation. Computer Methods and Programs in Biomedicine, 194, 1-17. https://doi.org/10.1016/j.cmpb.2020.105515
Saroj, S. K., Kumar, R., & Singh, N. P. (2022). Retinal blood vessels segmentation using Wald PDF and MSMO operator. Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 10(2), 1-18. https://doi.org/10.1080/21681163.2021.1989467
Saroj, S. K., Ratna, V., Kumar, R., & Singh, N. P. (2020). Efficient kernel based matched filter approach for segmentation of retinal blood vessels. Solid State Technology, 63(5), 7318-7334. https://doi.org/10.1016/j.sst.2020.05.001
Sengar, N., Joshi, R. C., Dutta, M. K., & Burget, R. (2023). EyeDeep-Net: A multi-class diagnosis of retinal diseases using deep neural network. Neural Computing and Applications, 35, 1-21. https://doi.org/10.1007/s00521-022-07345-1
Shrivastava, A., Kamble, R., Kulkarni, S., Singh, S., Hegde, A., Kashikar, R., & Das, T. (2021). Deep learning based ocular disease classification using retinal fundus images. Investigative Ophthalmology & Visual Science, 63(11). https://doi.org/10.1167/iovs.63.11.1
Staal, J., Abramoff, M. D., Niemeijer, M., Viergever, M. A., & Ginneken, B. V. (2004). Ridge based vessel segmentation in color images of the retina. IEEE Transaction on Medical Imaging, 23(4), 501-509. https://doi.org/10.1109/TMI.2004.825627
Tamura, H., Mori, S., & Yamawaki, T. (1978). Textural features corresponding to visual perception. IEEE Transactions on Systems, Man, and Cybernetics, 8(6), 460-473. https://doi.org/10.1109/TSMC.1978.4309999
Tolles, J., & Meurer, J. W. (2016). Logistic regression relating patient characteristics to outcomes. JAMA, 316(5), 533-534. https://doi.org/10.1001/jama.2016.7653
Virmani, J., Singh, G. P., Singh, Y., & Kriti. (2019). PNN-based classification of retinal diseases using fundus images. Sensors for Health Monitoring, 5, 215-242. https://doi.org/10.3390/s1909215
Zhu, S., Lu, B., Wang, C., Wu, M., Zheng, B., Jiang, Q., Wei, R., Cao, Q., & Yang, W. (2022). Screening of common retinal diseases using six-category models based on EfficientNet. Frontiers in Medicine, 9, 1-9. https://doi.org/10.3389/fmed.2022.1234567
Saroj, S. K., Kumar, R., & Singh, N. P. (2025). Machine Learning Based Prediction of Retinopathy Diseases Using Segmented Retinal Images. ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 14, e31737. https://doi.org/10.14201/adcaij.31737
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