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Optimised deep k-nearest neighbour’s based diabetic retinopathy diagnosis(ODeep-NN) using retinal images

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Abstract

Diabetes mellitus has been regarded as one of the prime health issues in present days, which can often lead to diabetic retinopathy, a complication of the disease that affects the eyes, causing loss of vision. For precisely detecting the condition’s existence, clinicians are required to recognise the presence of lesions in colour fundus images, making it an arduous and time-consuming task. To deal with this problem, a lot of work has been undertaken to develop deep learning-based computer-aided diagnosis systems that assist clinicians in making accurate diagnoses of the diseases in medical images. Contrariwise, the basic operations involved in deep learning models lead to the extraction of a bulky set of features, further taking a long period of training to predict the existence of the disease. For effective execution of these models, feature selection becomes an important task that aids in selecting the most appropriate features, with an aim to increase the classification accuracy. This research presents an optimised deep k-nearest neighbours’-based pipeline model in a bid to amalgamate the feature extraction capability of deep learning models with nature-inspired metaheuristic algorithms, further using k-nearest neighbour algorithm for classification. The proposed model attains an accuracy of 97.67 and 98.05% on two different datasets considered, outperforming Resnet50 and AlexNet deep learning models. Additionally, the experimental results also portray an analysis of five different nature-inspired metaheuristic algorithms, considered for feature selection on the basis of various evaluation parameters.

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Data availability

Dataset used in this article was obtained from the Kaggle (https://www.kaggle.com/sovitrath/diabetic-retinopathy-224x224-gaussian-filtered).

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Funding

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Authors and Affiliations

  1. Department of Computer Science and Engineering, DAV University, Jalandhar, Punjab, India

    Rahul Hans

  2. Department of Computer Science and Applications, DAV University, Jalandhar, Punjab, India

    Sanjeev Kumar Sharma

  3. School of Computing and Information Systems, University of Melbourne, Melbourne, Australia

    Uwe Aickelin

Authors
  1. Rahul Hans
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  2. Sanjeev Kumar Sharma
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  3. Uwe Aickelin
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Contributions

R.H. applied the algorithms and computed the results from the proposed model. S.K.S. performed the graphical analysis of the results. Both R.H. and S.K.S. worked on the development of the first draft of the manuscript. U.A. did the meticulous proofreading of the manuscript and suggested section wise improvements in the manuscript. All authors read and approved the final manuscript.

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Correspondence to Rahul Hans.

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Hans, R., Sharma, S.K. & Aickelin, U. Optimised deep k-nearest neighbour’s based diabetic retinopathy diagnosis(ODeep-NN) using retinal images. Health Inf Sci Syst 12, 23 (2024). https://doi.org/10.1007/s13755-024-00282-x

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