Detection of Microaneurysm in Retina Image using Machine Learning Approach

Diabetic Retinopathy (DR) one of the severe eye disorder which causes damage to capillaries in retina due to increase in blood sugar levels. Process which examines DR and its severity is presently performed by eye specialists due to the unavailability of good automated DR screening software. This paper proposes a method intended for early stage identification of DR with more accurate results compared to the existing methods. Key features extracted and classification using the extracted key features is performed using feed forward neural networks. For performing training and testing DIARETDB1 database is used. The Accuracy obtained is 98.89% for the proposed system.

[1]  Farida Cheriet,et al.  Red Lesion Detection Using Dynamic Shape Features for Diabetic Retinopathy Screening , 2016, IEEE Transactions on Medical Imaging.

[2]  Shehzad Khalid,et al.  Identification and classification of microaneurysms for early detection of diabetic retinopathy , 2013, Pattern Recognit..

[3]  Basant Kumar,et al.  Diabetic Retinopathy Detection by Extracting Area and Number of Microaneurysm from Colour Fundus Image , 2018, 2018 5th International Conference on Signal Processing and Integrated Networks (SPIN).

[4]  Bart M. ter Haar Romeny,et al.  Retinal Microaneurysms Detection Using Local Convergence Index Features , 2017, IEEE Transactions on Image Processing.

[5]  Bálint Antal,et al.  An Ensemble-Based System for Microaneurysm Detection and Diabetic Retinopathy Grading , 2012, IEEE Transactions on Biomedical Engineering.

[6]  S. Kumar,et al.  Automated lesion detectors in retinal fundus images , 2015, Comput. Biol. Medicine.

[7]  R. A. Welikala,et al.  Detection of microaneurysms in retinal images using an ensemble classifier , 2017 .

[8]  Qin Li,et al.  Retinopathy Online Challenge: Automatic Detection of Microaneurysms in Digital Color Fundus Photographs , 2010, IEEE Transactions on Medical Imaging.

[9]  Saeid Sanei,et al.  Localizing Microaneurysms in Fundus Images Through Singular Spectrum Analysis , 2017, IEEE Transactions on Biomedical Engineering.

[10]  Sabarna Choudhury,et al.  Filter and fuzzy c means based feature extraction and classification of diabetic retinopathy using support vector machines , 2016, 2017 International Conference on Communication and Signal Processing (ICCSP).

[11]  Lei Zhang,et al.  Sparse Representation Classifier for microaneurysm detection and retinal blood vessel extraction , 2012, Inf. Sci..

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

[13]  András Hajdu,et al.  Retinal Microaneurysm Detection Through Local Rotating Cross-Section Profile Analysis , 2013, IEEE Transactions on Medical Imaging.

[14]  D. K. Kole,et al.  Fuzzy C means based feature extraction and classification of diabetic retinopathy using support vector machines , 2016, 2016 International Conference on Communication and Signal Processing (ICCSP).

[15]  Dr. S. Poornachandra,et al.  Automated Microaneurysms Detection and Grading of Diabetic Retinopathy , 2014 .