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

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Abstract

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 Naïve Bayes classifier shows that the classification achieved by Random Forest classifier is 93.58% and it outperforms Naïve Bayes classifier which yields an accuracy of 83.63%.

Random Forest classifier for abnormality detection in retina images.

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Acknowledgements

The authors wish to acknowledge medical experts for fruitful consultations during the course of this work.

Funding

The authors received support for this research from a grant from Department of Biotechnology, Government of India (No.BT/PR4256/BID/7/393/2012 dated 02.08.2012).

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Authors and Affiliations

  1. Computer Science and Engineering Department, Maulana Abul Kalam Azad University of Technology, BF-142, Sector 1, Salt Lake City, Kolkata, West Bengal, 700064, India

    Amrita Roy Chowdhury

  2. Regional Institute of Ophthalmology, Calcutta Medical College, Kolkata, West Bengal, India

    Tamojit Chatterjee

  3. Department of Natural Science and IEM, Maulana Abul Kalam Azad University of Technology, BF-142, Sector 1, Salt Lake City, Kolkata, West Bengal, 700064, India

    Sreeparna Banerjee

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  1. Amrita Roy Chowdhury
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  2. Tamojit Chatterjee
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  3. Sreeparna Banerjee
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Correspondence to Sreeparna Banerjee.

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Chowdhury, A.R., Chatterjee, T. & Banerjee, S. A Random Forest classifier-based approach in the detection of abnormalities in the retina. Med Biol Eng Comput 57, 193–203 (2019). https://doi.org/10.1007/s11517-018-1878-0

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  • DOI: https://doi.org/10.1007/s11517-018-1878-0

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