Dr Miner: An Application of Auto Detecting Diabetic Retinopathy using Auto Colour Correlogramand Bagging


Yap, Chee Wei and Lim, Kai Jie and Ng, Keng Hoong and Khor, Kok Chin (2021) Dr Miner: An Application of Auto Detecting Diabetic Retinopathy using Auto Colour Correlogramand Bagging. Turkish Journal of Computer and Mathematics Education, 12 (3). pp. 1916-1922. ISSN 1309-4653

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An application of auto-detecting Diabetic Retinopathy (DR) is indispensable to aid the ophthalmologists in diagnosing patients and also to help relevant organisations in accumulating and analysing data. This project presents DR Miner, an application that can extract data from fundus images, identify the symptoms of DR in retina images by using data science approaches, and collect the ophthalmologist’s review to improve the detection model in the future. To form the DR data set with binary classes, Auto Colour Correlogram (ACC) was utilised to extract the features from DR images. Over-sampling was then conducted to balance the class distribution in the data set. To reduce the variance of the single learning algorithms, we evaluated various bagging approaches. Theresults showed that the bagging approaches gave better results than the single learning algorithms in general. Out of all bagging approaches we evaluated, bagged k-nearest neighbours gave the best result. The sensitivity achieved was 85.1%, which met the requirement set by the UK National Institute for Clinical Excellence.

Item Type: Article
Uncontrolled Keywords: Bagging, Auto Colour Correlogram, Diabetes Retinopathy
Subjects: T Technology > T Technology (General)
Divisions: Faculty of Computing and Informatics (FCI)
Depositing User: Ms Nurul Iqtiani Ahmad
Date Deposited: 16 Aug 2021 16:24
Last Modified: 16 Aug 2021 16:24
URII: http://shdl.mmu.edu.my/id/eprint/9261


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