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

Medical & biological engineering & computing, 2019, Vol.57 (1), p.193-203 [Peer Reviewed Journal]

International Federation for Medical and Biological Engineering 2018 ;Medical & Biological Engineering & Computing is a copyright of Springer, (2018). All Rights Reserved. ;ISSN: 0140-0118 ;EISSN: 1741-0444 ;DOI: 10.1007/s11517-018-1878-0 ;PMID: 30076537

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Title:
A Random Forest classifier-based approach in the detection of abnormalities in the retina
Author: Chowdhury, Amrita Roy ; Chatterjee, Tamojit ; Banerjee, Sreeparna
Subjects: Abnormalities ; Age ; Artificial intelligence ; Bayesian analysis ; Biomedical and Life Sciences ; Biomedical Engineering and Bioengineering ; Biomedicine ; CAI ; Classification ; Classifiers ; Computer Applications ; Computer assisted instruction ; Correlation analysis ; Correlation coefficient ; Correlation coefficients ; Diabetes ; Diabetes mellitus ; Diabetic retinopathy ; Diagnosis ; Human Physiology ; Image classification ; Image detection ; Image processing ; Image segmentation ; Imaging ; Learning algorithms ; Low level ; Machine learning ; Macular degeneration ; Medical imaging ; Original Article ; Radiology ; Retina ; Retinopathy ; Sensitivity analysis
Is Part Of: Medical & biological engineering & computing, 2019, Vol.57 (1), p.193-203
Description: 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%. Graphical abstract Random Forest classifier for abnormality detection in retina images.
Publisher: Berlin/Heidelberg: Springer Berlin Heidelberg
Language: English
Identifier: ISSN: 0140-0118
EISSN: 1741-0444
DOI: 10.1007/s11517-018-1878-0
PMID: 30076537
Source: ProQuest Central

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

International Federation for Medical and Biological Engineering 2018 ;Medical & Biological Engineering & Computing is a copyright of Springer, (2018). All Rights Reserved. ;ISSN: 0140-0118 ;EISSN: 1741-0444 ;DOI: 10.1007/s11517-018-1878-0 ;PMID: 30076537

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