Abstract
This paper presents a machine learning classifier, namely, Random Forest to detect abnormalities in retina arising from Diabetic Retinopathy. This is an effort to obtain a computer-aided diagnosis procedure to substitute manual detection. Fundus images from public datasets are used for this purpose. A set of statistical and geometric features were extracted from images in the database which contains the different physical manifestations of the disease. Classification through machine learning can help a physician by giving an indication of the level of the disease. The experimental results show 99.275% of accuracy in prediction of the disease, which is promising.
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Roychowdhury, A., Banerjee, S. (2018). Random Forests in the Classification of Diabetic Retinopathy Retinal Images. In: Bhattacharyya, S., Gandhi, T., Sharma, K., Dutta, P. (eds) Advanced Computational and Communication Paradigms. Lecture Notes in Electrical Engineering, vol 475. Springer, Singapore. https://doi.org/10.1007/978-981-10-8240-5_19
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DOI: https://doi.org/10.1007/978-981-10-8240-5_19
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