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Emperor penguin optimization algorithm- and bacterial foraging optimization algorithm-based novel feature selection approach for glaucoma classification from fundus images

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Abstract

Feature selection is an important component of the machine learning domain, which selects the ideal subset of characteristics relative to the target data by omitting irrelevant data. For a given number of features, there are 2n possible feature subsets, making it challenging to select the optimal set of features from a dataset via conventional feature selection approaches. We opted to investigate glaucoma infection since the number of individuals with this disease is rising quickly around the world. The goal of this study is to use the feature set (features derived from fundus images of benchmark datasets) to classify images into two classes (infected and normal) and to select the fewest features (feature selection) to achieve the best performance on various efficiency measuring metrics. In light of this, the paper implements and recommends a metaheuristics-based technique for feature selection based on emperor penguin optimization, bacterial foraging optimization, and proposes their hybrid algorithm. From the retinal fundus benchmark images, a total of 36 features were extracted. The proposed technique for selecting features minimizes the number of features while improving classification accuracy. Six machine learning classifiers classify on the basis of a smaller subset of features provided by these three optimization techniques. In addition to the execution time, eight statistically based performance metrics are calculated. The hybrid optimization technique combined with random forest achieves the highest accuracy, up to 0.95410. Because the proposed medical decision support system is effective and ensures trustworthy decision-making for glaucoma screening, it might be utilized by medical practitioners as a second opinion tool, as well as assist overworked expert ophthalmologists and prevent individuals from losing their eyesight.

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Data availability

The datasets analyzed during the current study are easily and publically available in the internet’s repository. We also confirm that data will be available on demand.

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Authors and Affiliations

  1. Department of Computer Engineering and Applications, G.L.A University, Mathura, 281406, India

    Law Kumar Singh & Hitendra Garg

  2. Department of Computer Science and Engineering, Hindustan College of Science and Technology, Mathura, 281122, India

    Munish Khanna

  3. Department of Physics, Uttar Pradesh Rajarshi Tandon Open University, Prayagraj, Uttar Pradesh, India

    Rekha Singh

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All authors contributed equally to the study conception, design and coding. Material preparation, data collection and analysis were performed by [LKS], [MK], [HG] and [RS]. All authors read and approved the final submitted manuscript.

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Singh, L.K., Khanna, M., Garg, H. et al. Emperor penguin optimization algorithm- and bacterial foraging optimization algorithm-based novel feature selection approach for glaucoma classification from fundus images. Soft Comput 28, 2431–2467 (2024). https://doi.org/10.1007/s00500-023-08449-6

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