SVM and Neural Network based Diagnosis of Diabetic Retinopathy

Diabetic retinopathy (DR) is an eye disease caused by the complication of diabetes and we should detect it early for effective treatment. As diabetes progresses, the vision of a patient may start deteriorate and lead to diabetic retinopathy. As a result, two groups were identified, namely nonproliferative diabetic retinopathy (NPDR) and proliferative diabetic retinopathy (PDR). In this paper, to diagnose diabetic retinopathy, two models like Probabilistic Neural network (PNN) and Support vector machine (SVM) are described and their performances are compared. Experimental results show that PNN has an accuracy of 89.60% and SVM has an accuracy of 97.608 %. This infers that the SVM model outperforms the other model.

[1]  Ana Maria Mendonça,et al.  Automatic segmentation of microaneurysms in retinal angiograms of diabetic patients , 1999, Proceedings 10th International Conference on Image Analysis and Processing.

[2]  Elif Derya.  Multiclass Support Vector Machines for EEG-Signals Classification , 2007 .

[3]  Donald F. Specht,et al.  Probabilistic neural networks , 1990, Neural Networks.

[4]  O. Chutatape,et al.  Fundus image features extraction , 2000, Proceedings of the 22nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (Cat. No.00CH37143).

[5]  Elif Derya Übeyli,et al.  Multiclass Support Vector Machines for EEG-Signals Classification , 2007, IEEE Trans. Inf. Technol. Biomed..

[6]  Hung T. Nguyen,et al.  Classification of diabetic retinopathy using neural networks , 1996, Proceedings of 18th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[7]  Majid Mirmehdi,et al.  Classification and Localisation of Diabetic-Related Eye Disease , 2002, ECCV.

[8]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[9]  Chanjira Sinthanayothin,et al.  Automated screening system for diabetic retinopathy , 2003, 3rd International Symposium on Image and Signal Processing and Analysis, 2003. ISPA 2003. Proceedings of the.

[10]  T. K. Bandopadhyaya,et al.  Journal of Theoretical and Applied Information Technology , 2022 .

[11]  Chanin Nantasenamat,et al.  Data mining of magnetocardiograms for prediction of ischemic heart disease , 2010, EXCLI journal.

[12]  Rui J. P. de Figueiredo,et al.  Automatic Detection and Diagnosis of Diabetic Retinopathy , 2007, 2007 IEEE International Conference on Image Processing.

[13]  Jie Tian,et al.  Classification of Underwater Objects Based on Probabilistic Neural Network , 2009, 2009 Fifth International Conference on Natural Computation.

[14]  V. Vijaya Kumari,et al.  Feature Extraction for Early Detection of Diabetic Retinopathy , 2010, 2010 International Conference on Recent Trends in Information, Telecommunication and Computing.

[15]  E. Chaum,et al.  A Probabilistic Framework for Content-Based Diagnosis of Retinal Disease , 2007, 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[16]  M. Goldbaum,et al.  Detection of blood vessels in retinal images using two-dimensional matched filters. , 1989, IEEE transactions on medical imaging.

[17]  Pascale Massin,et al.  A contribution of image processing to the diagnosis of diabetic retinopathy-detection of exudates in color fundus images of the human retina , 2002, IEEE Transactions on Medical Imaging.

[18]  D. Feng,et al.  IEEE transactions on information technology in biomedicine: special issue on advances in clinical and health-care knowledge management , 2005 .

[19]  M. Larsen,et al.  Automated detection of fundus photographic red lesions in diabetic retinopathy. , 2003, Investigative ophthalmology & visual science.

[20]  Shuqian Luo,et al.  Support vector machine based method for identifying hard exudates in retinal images , 2009, 2009 IEEE Youth Conference on Information, Computing and Telecommunication.

[21]  Zhiming Cui,et al.  Research on Cerebral Aneurysm Image Recognition Method Using Bayesian Classification , 2009 .

[22]  Wahyu Kusuma,et al.  Journal of Theoretical and Applied Information Technology , 2012 .

[23]  Allam Appa Rao,et al.  A PROBABILISTIC NEURAL NETWORK APPROACH FOR PROTEIN SUPERFAMILY CLASSIFICATION , 2009 .

[24]  G. Ravindran,et al.  Diabetic retinopathy classification , 2003, TENCON 2003. Conference on Convergent Technologies for Asia-Pacific Region.

[25]  K. Namuduri,et al.  Automated detection and classification of vascular abnormalities in diabetic retinopathy , 2004, Conference Record of the Thirty-Eighth Asilomar Conference on Signals, Systems and Computers, 2004..