A Random Forest classifier-based approach in the detection of abnormalities in the retina.
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-aid...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 57; no. 1; pp. 193 - 204 |
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| Autores principales: | , , |
| Formato: | pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
Jan2019
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=133800692&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 133800692 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Jan2019 vid: 57 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 133800692 133800692 NLM30076537 133800692 10.1007/s11517-018-1878-0 NLM30076537 133800692 ppf: 193 ppct: 11 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: A Random Forest classifier-based approach in the detection of abnormalities in the retina. aug: au: Chowdhury, Amrita Roy Chatterjee, Tamojit Banerjee, Sreeparna affil: Computer Science and Engineering Department, Maulana Abul Kalam Azad University of Technology, BF-142, Sector 1, Salt Lake City, 700064, Kolkata, West Bengal, India sug: subj: Algorithms Retina Abnormalities ROC Curve Probability Funding Source ab: 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. pubtype: Academic Journal doctype: pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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