An Image Mining Technique Using Support Vector Machine Based Retinal Image Classification

Authors

  • Arulkothaipriya M Department of Computer Science, National College, Trichy, India

Keywords:

Image processing, Eye components, Disease diagnosis, Cardio vascular diseases, Classification, Support Vector machine

Abstract

The attention is routinely mentioned to furnish a window into the health of a person for it's only in the e Diabetic retinopathy (DR) is a significant eye disease originating from diabetes mellitus ye that one can surely see the exposed flesh of the subject without utilizing invasive tactics. There are quantities of diseases, primarily vascular disorder that depart telltale markers within the retina. Micro aneurysms (MAs) are early signs of DR, so the detection of these lesions is predominant in an efficient screening application to satisfy medical protocols. Retinal photos provide enormous knowledge on pathological alterations brought on via regional ocular disorder which exhibits diabetes, hypertension, arteriosclerosis, cardiovascular disease and stroke. Computer-aided evaluation of retinal picture performs a significant position in diagnostic procedures. Nonetheless, computerized retinal segmentation is problematic by means of the fact that retinal photographs are by and large noisy, poorly contrasted, and the vessel widths can fluctuate from very giant to very small. This paper grants photo processing systems similar to darkish object detection to analyze the situation or increase the enter photograph so as to make it suitable for further processing and beef up the visibility of vessels in color fungus portraits. Then we are able to put in force okay-way clustering algorithm to segment the vessels and automate classification procedure headquartered on support vector computing device to provide regional know-how about arteries and veins. And finally predict cardio vascular diseases and other ailments utilizing CRAE and CRVE measurements

References

[1] Abdallah, Mariem Ben, et al. "Automatic extraction of blood vessels in the retinal vascular tree using multiscalemedialness." Journal of Biomedical Imaging 2015 (2015): 1.

[2] Kaur, Manvir, and Rajneesh Talwar. "Automatic Extraction of Blood Vessel and Eye Retinopathy Detection." European Journal of Advances in Engineering and Technology 2.4 (2015): 57-61.

[3] Wang, Shuangling, et al. "Hierarchical retinal blood vessel segmentation based on feature and ensemble learning." Neurocomputing 149 (2015): 708-717.

[4]Vidyashree, M. R., M. V. Usha, and vtuewit."Locating the optic nerve and blood vessel in a retinal images using graph partition method." (2015).

[5] Annunziata, Roberto, et al. "Leveraging multiscale hessian-based enhancement with a novel exudate inpainting technique for retinal vessel segmentation." IEEE journal of biomedical and health informatics 20.4 (2016): 1129-1138.

[6] N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, “Dropout: A simple way to prevent neural networks from overfitting,” The Journal of Machine Learning Research, vol. 15, no. 1, pp. 1929–1958, 2014.

[7] Y. Bengio, P. Lamblin, D. Popovici, H. Larochelle et al., “Greedy layer-wise training of deep networks,” Advances in neural information processing systems, vol. 19, p. 153, 2007.

[8] M. Fraz, P. Remagnino, A. Hoppe, B. Uyyanonvara, A. Rudnicka, C. Owen, and S. Barman, “Blood vessel segmentation methodologies in retinal images - a survey,” Comput. Methods Prog.Biomed. vol. 108, no. 1, pp. 407–433, Oct. 2012. [Online] Available: http://dx.doi.org/10.1016/j.cmpb.2012.03.009

[9] J. Staal, M. D. Abràmoff, M. Niemeijer, M. A. Viergever, and B. van Ginneken, “Ridge-based vessel segmentation in color images of the retina,” Medical Imaging, IEEE Transactions on, vol. 23, no. 4, pp. 501–509, 2004.

[10] A. Hoover, V. Kouznetsova, and M. Goldbaum, “Locating blood vessels in retinal images by piecewise threshold probing of a matched filter response,” Medical Imaging, IEEE Transactions on, vol. 19, no. 3, pp. 203–210, 2000.

Downloads

Published

2025-11-18

How to Cite

[1]
M. Arulkothaipriya, “An Image Mining Technique Using Support Vector Machine Based Retinal Image Classification”, Int. J. Comp. Sci. Eng., vol. 6, no. 11, pp. 63–69, Nov. 2025.

Issue

Vol. 6 No. 11 (2018): IJCSE Special Issue December Edition

Section

Research Article

License

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Authors contributing to this journal agree to publish their articles under the Creative Commons Attribution 4.0 International License, allowing third parties to share their work (copy, distribute, transmit) and to adapt it, under the condition that the authors are given credit and that in the event of reuse or distribution, the terms of this license are made clear.