Authors:
Shaik Akbar,Divya Midhunchakkaravarthy,DOI NO:
https://doi.org/10.26782/jmcms.2020.02.00009Keywords:
Diabetic Retinopathy,Deep learning,Feature Extraction,Classification,Abstract
Diabetic retinopathy (DR) is one of the eye diseases that results in vision loss if not diagnosed earlier. The automated computer aided models on the DR images help in accurate treatment disease prevention. Microaneurysms (MA) and red spots are the indicators of DR for disease diagnosis. Many DR classification approaches have been proposed in the literature with deep learning framework and non-linear functionality. Also, these models are not applicable to large feature space due to high true negative rate. To optimize these problems, a hybrid feature selection based deep learning classifier is used to detect the MA and red spots disease severity on the large image dataset. In this paper, a new feature extraction approach is implemented to find the essential positive bag features to the deep learning framework. A hybrid SVM classification model is used to classify the disease patterns with high true positive rate. Experimental results are simulated on different DR image class labels; results show that the hybrid deep learning classification model is better than the traditional models under various statistical metrics on large dataset.Refference:
I. B. Dashtbozorg, J. Zhang, F. Huang, and B. M. ter Haar Romeny, “Retinal Microaneurysms Detection Using Local Convergence Index Features,” IEEE Transactions on Image Processing, vol. 27, no. 7, pp. 3300–3315, Jul. 2018.
II. J. Xu et al., “Automatic Analysis of Microaneurysms Turnover to Diagnose the Progression of Diabetic Retinopathy,” IEEE Access, vol. 6, pp. 9632–9642, 2018.
III. K. M. Adal, P. G. van Etten, J. P. Martinez, K. W. Rouwen, K. A. Vermeer, and L. J. van Vliet, “An Automated System for the Detection and Classification of Retinal Changes Due to Red Lesions in Longitudinal Fundus Images,” IEEE Transactions on Biomedical Engineering, vol. 65, no. 6, pp. 1382–1390, Jun. 2018.
IV. L. Zhou, Y. Zhao, J. Yang, Q. Yu, and X. Xu, “Deep multiple instance learning for automatic detection of diabetic retinopathy in retinal images,” IET Image Processing, vol. 12, no. 4, pp. 563–571, 2018.
V. M. Leeza and H. Farooq, “Detection of severity level of diabetic retinopathy using Bag of features model,” IET Computer Vision, vol. 13, no. 5, pp. 523–530, 2019.
VI. P. Costa, A. Galdran, A. Smailagic, and A. Campilho, “A Weakly-Supervised Framework for Interpretable Diabetic Retinopathy Detection on Retinal Images,” IEEE Access, vol. 6, pp. 18747–18758, 2018.
VII. R. Bhoopalan and S. Sundaramoorthy, “Efficient approach for the automatic detection of haemorrhages in colour retinal images,” IET Image Processing, vol. 12, no. 9, pp. 1540–1544, 2018.
VIII. R. F. Mansour, “Evolutionary Computing Enriched Computer-Aided Diagnosis System for Diabetic Retinopathy: A Survey,” IEEE Reviews in Biomedical Engineering, vol. 10, pp. 334–349, 2017.
IX. R. Pires, S. Avila, H. F. Jelinek, J. Wainer, E. Valle, and A. Rocha, “Beyond Lesion-Based Diabetic Retinopathy: A Direct Approach for Referral,” IEEE Journal of Biomedical and Health Informatics, vol. 21, no. 1, pp. 193–200, Jan. 2017.
X. S. Lahmiri, “High-frequency-based features for low and high retina haemorrhage classification,” Healthcare Technology Letters, vol. 4, no. 1, pp. 20–24, 2017.
XI. S. Manivannan, C. Cobb, S. Burgess, and E. Trucco, “Subcategory Classifiers for Multiple-Instance Learning and Its Application to Retinal Nerve Fiber Layer Visibility Classification,” IEEE Transactions on Medical Imaging, vol. 36, no. 5, pp. 1140–1150, May 2017.
XII. S. Qummar et al., “A Deep Learning Ensemble Approach for Diabetic Retinopathy Detection,” IEEE Access, vol. 7, pp. 150530–150539, 2019.
XIII. S. Sil Kar and S. P. Maity, “Gradation of diabetic retinopathy on reconstructed image using compressed sensing,” IET Image Processing, vol. 12, no. 11, pp. 1956–1963, 2018.
XIV. Seoud, T. Hurtut, J. Chelbi, F. Cheriet, and J. M. P. Langlois, “Red Lesion Detection Using Dynamic Shape Features for Diabetic Retinopathy Screening,” IEEE Transactions on Medical Imaging, vol. 35, no. 4, pp. 1116–1126, Apr. 2016.
XV. W. Cao, N. Czarnek, J. Shan, and L. Li, “Microaneurysm Detection Using Principal Component Analysis and Machine Learning Methods,” IEEE Transactions on NanoBioscience, vol. 17, no. 3, pp. 191–198, Jul. 2018.
XVI. W. Zhou, C. Wu, D. Chen, Y. Yi, and W. Du, “Automatic Microaneurysm Detection Using the Sparse Principal Component Analysis-Based Unsupervised Classification Method,” IEEE Access, vol. 5, pp. 2563–2572, 2017.
XVII. Zeng, H. Chen, Y. Luo, and W. Ye, “Automated Diabetic Retinopathy Detection Based on Binocular Siamese-Like Convolutional Neural Network,” IEEE Access, vol. 7, pp. 30744–30753, 2019.