4.5 Article

A Random Forest classifier-based approach in the detection of abnormalities in the retina

Journal

MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING
Volume 57, Issue 1, Pages 193-203

Publisher

SPRINGER HEIDELBERG
DOI: 10.1007/s11517-018-1878-0

Keywords

Random Forest classifier; Naive Bayes classifier; Diabetic retinopathy images; Age-related macular degeneration; K-means clustering

Funding

  1. Department of Biotechnology, Government of India [BT/PR4256/BID/7/393/2012]

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Classification of abnormalities from medical images using computer-based approaches is of growing interest in medical imaging. Timely detection of abnormalities due to diabetic retinopathy and age-related macular degeneration is required in order to prevent the prognosis of the disease. Computer-aided systems using machine learning are becoming interesting to ophthalmologists and researchers. We present here one such technique, the Random Forest classifier, to aid medical practitioners in accurate diagnosis of the diseases. A computer-aided diagnosis system is proposed for detecting retina abnormalities, which combines K means-based segmentation of the retina image, after due preprocessing, followed by machine learning techniques, using several low level and statistical features. Abnormalities in the retina that are classified are caused by age-related macular degeneration and diabetic retinopathy. Performance measures used in the analysis are accuracy, sensitivity, specificity, F-measure, and Mathew correlation coefficient. A comparison with another machine learning technique, the Naive Bayes classifier shows that the classification achieved by Random Forest classifier is 93.58% and it outperforms Naive Bayes classifier which yields an accuracy of 83.63%.

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