Eye Diseases Detection and Classification in Fundus Image Database with Optimization Model in Machine Learning Architecture
Keywords:
Eye diseases, Feature Extraction, Classification, Diabetic Retinopathy (DR), maculopathy, Glaucoma, Exudates and Hemorrhage, Optimization.Abstract
In recent years, diabetes rates are increasing drastically due to elevated blood sugar in the human body. With the increase in diabetes rate, it impacts the eye it requires regular examination to prevent blindness. Eye diseases affects the person with a higher glucose rate in the blood. However, after certain duration blood sugar remains in the retina and affects the retina lead to damage in the eye. The presence of blood glucose in the vessels of the eye damages the eye vessels and causes leakage of fluid. Eye diseases’ impact on working-age adults causes the loss of eyesight. Even though treatment can help but early intervention prevents loss of vision due to eye diseases such as Diabetic Retinopathy (DR), maculopathy, Glaucoma, Exudates, and Hemorrhage. This paper proposed an Entropy segmentation Survival Analysis Optimization (EsSO) for the classification of Diabetic Retinopathy (DR), maculopathy, Glaucoma, Exudates, and Hemorrhage. The proposed architecture performs segmentation based on the estimation of entropy. The feature extraction and classification are performed with the optimization of the GLCM features in the images. To perform image optimization GLCM features with the black widow are implemented. Through computed feature classification is performed with the conventional neural network model for classification. The classification is performed for estimation of different diseases in the eye Diabetic Retinopathy (DR), maculopathy, Glaucoma, Exudates and Hemorrhage. The proposed EsSO model concentrated highly on the intervention of eye diseases for diagnosis and treatment. The performance of the developed model is comparatively examined with the conventional technique. The proposed EsSO model provides an accuracy of 96% whereas the conventional classifiers SVM and RF provides the accuracy of 91% and 94% respectively. The evaluation expressed that the proposed EsSO model exhibits ~4% improvement than the conventional classifiers.
Downloads
References
Vujosevic, S., Aldington, S. J., Silva, P., Hernández, C., Scanlon, P., Peto, T., &Simó, R. (2020). Screening for Eye diseases: new perspectives and challenges. The Lancet Diabetes & Endocrinology, 8(4), 337-347.
Rübsam, A., Parikh, S., & Fort, P. E. (2018). Role of inflammation in Eye diseases. International journal of molecular sciences, 19(4), 942.
Qummar, S., Khan, F. G., Shah, S., Khan, A., Shamshirband, S., Rehman, Z. U., ... &Jadoon, W. (2019). A deep learning ensemble approach for Eye diseases detection. IEEE Access, 7, 150530-150539.
Porwal, P., Pachade, S., Kamble, R., Kokare, M., Deshmukh, G., Sahasrabuddhe, V., &Meriaudeau, F. (2018). Indian Eye diseases image dataset (IDRiD): a database for Eye diseases screening research. Data, 3(3), 25.
Gadekallu, T. R., Khare, N., Bhattacharya, S., Singh, S., Maddikunta, P. K. R., & Srivastava, G. (2020). Deep neural networks to predict Eye diseases. Journal of Ambient Intelligence and Humanized Computing, 1-14.
Sabanayagam, C., Banu, R., Chee, M. L., Lee, R., Wang, Y. X., Tan, G., ... & Wong, T. Y. (2019). Incidence and progression of Eye diseases: a systematic review. The lancet Diabetes & endocrinology, 7(2), 140-149.
Qiao, L., Zhu, Y., & Zhou, H. (2020). Eye diseases detection using prognosis of microaneurysm and early diagnosis system for non-proliferative Eye diseases based on deep learning algorithms. IEEE Access, 8, 104292-104302.
Deperlıoğlu, Ö., &Köse, U. (2018, October). Diagnosis of Eye diseases by using image processing and convolutional neural network. In 2018 2nd International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT) (pp. 1-5). IEEE.
Palavalasa, K. K., &Sambaturu, B. (2018, April). Automatic Eye diseases detection using digital image processing. In 2018 International Conference on Communication and Signal Processing (ICCSP) (pp. 0072-0076). IEEE.
Hemanth, D. J., Deperlioglu, O., &Kose, U. (2020). An enhanced Eye diseases detection and classification approach using deep convolutional neural network. Neural Computing and Applications, 32(3), 707-721.
Gharaibeh, N., Al-Hazaimeh, O. M., Al-Naami, B., & Nahar, K. M. (2018). An effective image processing method for detection of Eye diseasesdiseases from retinal fundus images. International Journal of Signal and Imaging Systems Engineering, 11(4), 206-216.
Rahim, S. S., Palade, V., &Holzinger, A. (2020). Image processing and machine learning techniques for Eye diseases detection: a review. Artificial Intelligence and Machine Learning for Digital Pathology, 136-154.
Qureshi, I., Ma, J., & Abbas, Q. (2019). Recent development on detection methods for the diagnosis of Eye diseases. Symmetry, 11(6), 749.
Shankar, K., Zhang, Y., Liu, Y., Wu, L., & Chen, C. H. (2020). Hyperparameter tuning deep learning for Eye diseases fundus image classification. IEEE Access, 8, 118164-118173
Ratanapakorn, T., Daengphoonphol, A., Eua-Anant, N., &Yospaiboon, Y. (2019). Digital image processing software for diagnosing Eye diseases from fundus photograph. Clinical Ophthalmology (Auckland, NZ), 13, 641.
Ratanapakorn, T., Daengphoonphol, A., Eua-Anant, N., &Yospaiboon, Y. (2019). Digital image processing software for diagnosing Eye diseases from fundus photograph. Clinical Ophthalmology (Auckland, NZ), 13, 641.
Chakrabarty, N. (2018, November). A deep learning method for the detection of Eye diseases. In 2018 5th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON) (pp. 1-5). IEEE.
Gadekallu, T. R., Khare, N., Bhattacharya, S., Singh, S., Maddikunta, P. K. R., & Srivastava, G. (2020). Deep neural networks to predict Eye diseases. Journal of Ambient Intelligence and Humanized Computing, 1-14.
Zhang, W., Zhong, J., Yang, S., Gao, Z., Hu, J., Chen, Y., & Yi, Z. (2019). Automated identification and grading system of Eye diseases using deep neural networks. Knowledge-Based Systems, 175, 12-25.
Kandel, I., & Castelli, M. (2020). Transfer learning with convolutional neural networks for Eye diseases image classification. A review. Applied Sciences, 10(6), 2021.
Ramachandran, N., Hong, S. C., Sime, M. J., & Wilson, G. A. (2018). Eye diseases screening using deep neural network. Clinical & experimental ophthalmology, 46(4), 412-416.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
