Citation

Yap, Chew Wai and Lim, Kai Jie and Ng, Keng Hoong and Khor, Kok Chin (2020) Dr Miner: An Application of Auto Detecting Diabetic Retinopathy using Auto Colour Correlogram and Bagging. In: 2nd Applied Informatics International Conference 2020, 12-13 August 2020, Online Conference.

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Abstract

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 is able to 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, various bagging approaches had been evaluated. It was found 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:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	Bagging, Auto Colour Correlogram, Diabetes Retinopathy
Subjects:	T Technology > T Technology (General)
Divisions:	Faculty of Computing and Informatics (FCI)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	10 Sep 2021 14:35
Last Modified:	10 Sep 2021 14:35
URII:	http://shdl.mmu.edu.my/id/eprint/8514

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