Detection of Abnormalities of Retina Due to Diabetic Retinopathy and Age Related Macular Degeneration Using SVM
Science Journal of Circuits, Systems and Signal Processing
Volume 5, Issue 1, February 2016, Pages: 1-7
Received: May 18, 2016;
Published: May 19, 2016
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Amrita Roy Chowdhury, Computer Science and Engineering Department, West Bengal University of Technology, Salt Lake, India
Sreeparna Banerjee, Computer Science and Engineering Department, West Bengal University of Technology, Salt Lake, India; Natural Science department, West Bengal University of Technology, Salt Lake, India
Diabetic Retinopathy and Age related Macular Degeneration are two major retinal diseases which are creating serious concern in today’s age. Detection of preliminary signs of abnormalities due to these diseases is hard and time consuming for the ophthalmologists as the abnormal objects are very fine and small in size. As early detection of abnormalities can prevent permanent vision loss, a semi automated system is developed to detect the affected portion of retina and is tested with some retinal images. A training image set is used to train a support vector machine classifier. A test image set is given to the classifier for automatic detection of disease type. Efficiency of the classifier is tested comparing the actual value and predicted value by the classifier.
Amrita Roy Chowdhury,
Detection of Abnormalities of Retina Due to Diabetic Retinopathy and Age Related Macular Degeneration Using SVM, Science Journal of Circuits, Systems and Signal Processing.
Vol. 5, No. 1,
2016, pp. 1-7.
A. Roy Chowdhury, R. Saha, S. Banerjee, Detection of different types of Diabetic Retinopathy and Age related Macular Degeneration, Computer Science and Application (CSA) 2015 Proceedings, pp: 71-76.
M. Niemeijer, B. V. Ginneken, S. R. Russell, M. S. A. Suttorp-Schulten, and M. D. Abramoff, Automated Detection and Differentiation of Drusen, Exudates, and Cotton-Wool Spots in Digital Color Fundus Photographs for Diabetic Retinopathy Diagnosis, Vol. 48, No. 5 (Invest Ophthalmol Visual Science., May 2007).
A. Sopharak, M. N. Dailey, B. Uyyanonvara, S. Barman, T. Williamson, K. T. Nwe and Y. A. Moe, Machine learning approach to automatic exudate detection in retinal images from diabetic patients, Vol. 57(2), pp:124-135, 2009 (Journal of Modern Optics).
S. Banerjee, A. Roy Chowdhury, Case Based Reasoning in the Detection of Retinal Abnormalities using Decision Trees, International Conference on Information and Communication Technologies (ICICT 2014).
A. Sopharak, B. Uyyanonvara and S. Barman, Automatic Exudate Detection from Non-dilated Diabetic Retinopathy Retinal Images Using Fuzzy C-means Clustering, Vol. 9, pp: 2148-2161, March 2009 (Sensors (1424-8220)).
T. Akila, G. Kavitha, Detection and Classification of Hard Exudates in Human Retinal Fundus Images Using Clustering and Random Forest Methods, Vol. 4, Special Issue 2, April (International Journal of Emerging Technology and Advanced Engineering, 2014).
C. Aravind, M. PonniBala, S. Vijayachitra, Automatic Detection of Microaneurysms and Classification of Diabetic Retinopathy Images using SVM Technique, (International Journal of Computer Applications (0975–8887)).
K. Wisaeng, N. Hiransakolwong, E. Pothiruk, Automatic Detection of Retinal Exudates using a Support Vector Machine, Vol. 32, No. 1/2013, pp: 33-42 (Applied Medical Informatics, 2013).
G. B. Kande, T. S. Savithri and P. V. Subbaiah, Automatic Detection of Microaneurysms and Hemorrhages in Digital Fundus Images, Vol 23, No 4 (August), pp 430-437 (Journal of Digital Imaging, 2010).
B. Ramasubramanian, G. Prabhakar, An Early Screening System for the Detection of Diabetic Retinopathy using Image Processing, Vol. 61– No.15, January 2013(International Journal of Computer Applications (0975–8887)).
B. Ramasubramanian, G. Mahendran, An Efficient Integrated Approach for the Detection of Exudates and Diabetic Maculopathy in Colour fundus Images, Advanced Computing: An International Journal ( ACIJ ), Vol.3, No.5, September 2012, DOI: 10.5121/acij.2012.3509 83.
Data Mining: Practical Machine Learning Tools and Techniques, Ian H. Witten, Eibe Frank, Mark A. Hall.