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Discrete ripplet-II transform feature extraction and metaheuristic-optimized feature selection for enhanced glaucoma detection in fundus images using least square-support vector machine

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Abstract

Recently, significant progress has been made in developing computer-aided diagnosis (CAD) systems for identifying glaucoma abnormalities using fundus images. Despite their drawbacks, methods for extracting features such as wavelets and their variations, along with classifier like support vector machines (SVM), are frequently employed in such systems. This paper introduces a practical and enhanced system for detecting glaucoma in fundus images. The proposed model adresses the chanallages encountered by other existing models in recent litrature. Initially, we have employed contrast limited adaputive histogram equalization (CLAHE) to enhanced the visualization of input fundus inmages. Then, the discrete ripplet-II transform (DR2T) employing a degree of 2 for feature extraction. Afterwards, we have utilized a golden jackal optimization algorithm (GJO) employed to select the optimal features to reduce the dimension of the extracted feature vector. For classification purposes, we have employed a least square support vector machine (LS-SVM) equipped with three kernels: linear, polynomial, and radial basis function (RBF). This setup has been utilized to classify fundus images as either indicative of glaucoma or healthy. The proposed method is validated with the current state-of-the-art models on two standard datasets, namely, G1020 and ORIGA. The results obtained from our experimental result demonstrate that our best suggested approach DR2T+GJO+LS-SVM-RBF obtains better classification accuracy 93.38% and 97.31% for G1020 and ORIGA dataset with less number of features. It establishes a more streamlined network layout compared to conventional classifiers.

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Funding

The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, C.V. Raman Global University, Odisha, India

    Santosh Kumar Sharma, Debendra Muduli, Adyasha Rath & Ganapati Panda

  2. Department of Information Technology, School of Engineering and Technology, Nagaland University, Dimapur, India

    Sujata Dash

  3. Department of Cyber Systems Engineering, WMG, University of Warwick, Coventry, CV74AL, UK

    Achyut Shankar

  4. University Centre for Research & Development, Chandigarh University, Mohali, Punjab, 140413, India

    Achyut Shankar

  5. School of Computer Science Engineering, Lovely Professional University, Phagwara, 144411, Punjab, India

    Achyut Shankar

  6. Center of Research Impact and Outcome, Chitkara University, Rajpura, Punjab, India

    Achyut Shankar

  7. Department of Computer Science & Engineering, Graphic Era Deemed to be University, Dehradun, Uttarakhand, 248002, India

    Achyut Shankar & Dinesh Chandra Dobhal

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  1. Santosh Kumar Sharma
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  2. Debendra Muduli
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Contributions

Santosh Kumar Sharma: Conceptualization, Methodology, Investigation, Writing- Original draft preparation, Software. Debendra Muduli: Supervision, Validation, Writing - Review and Editing. Adyasha Rath: Validation, Resources and Editing. Sujata Dash: Validation, Resources and Editing. Ganapati Panda: Validation, Resources and Editing. Achyut Shankar: Validation, Resources and Editing. Dinesh Chandra Dobhal: Validation, Resources and Editing.

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Correspondence to Debendra Muduli.

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Sharma, S.K., Muduli, D., Rath, A. et al. Discrete ripplet-II transform feature extraction and metaheuristic-optimized feature selection for enhanced glaucoma detection in fundus images using least square-support vector machine. Multimed Tools Appl 84, 26447–26479 (2025). https://doi.org/10.1007/s11042-024-19974-3

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