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Nature-inspired computing and machine learning based classification approach for glaucoma in retinal fundus images

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Abstract

Glaucoma, commonly known as the silent thief of sight, is the second most common cause of blindness in humans, and the number of cases is steadily increasing. Conventional diagnostic methods utilized by ophthalmologists include the assessment of intraocular pressure using tonometry, pachymetry, etc. Yet, each of these evaluations is time-consuming, requires human involvement, and is prone to subjective errors. In order to overcome these hurdles, practitioners are studying retinal pictures for glaucoma diagnosis within the field of medical imaging. In addition, computer-assisted diagnosis (CAD) systems can be created to solve these obstacles by using machine learning approaches to classify retinal pictures as "healthy" or "infected." This work presents a reduced set of structural and nonstructural features(characteristics) to characterize pictures of the retinal fundus. The grey level co-occurrence matrix (GLCM), the grey level run length matrix (GLRM), the first order statistical matrix (FOS), the wavelet, and the structural features (like disc damage likelihood scale (DDLS) and cup to disc ratio (CDR)) are extracted. This set of features is sent to three classical soft computing algorithms (Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), and Binary Cuckoo Search (BCS)) and their two-layered model (PSO-ABC) to generate subset of reduced features (feature selection phase) that computes auspicious accuracy when sent to three machine learning classifiers (Random Forest (RF), Support Vector Machine (SVM), and Ensemble of RF, SVM, and Logistic Regression). According to our understanding, these four soft computing algorithms are rarely employed in this application field. For analyzing the performance of suggested strategy, the ORIGA, REFUGE, and their combinations are chosen as subject datasets. Standard statistical performance indicators, including accuracy, specificity, precision, and sensitivity, are calculated. The BCS delivers remarkable performance with a minimum of 91% accuracy and a maximum of 98.46% accuracy. PSO-ABC heavily decreases the original feature set, with minor accuracy sacrifices. The quantitative results are also compared in light of the most recent state-of-the-art published research. Owing to its exemplary performance, the suggested method will undoubtedly serve as a second opinion for ophthalmologists.

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Availability of data and material

The data that support the findings of this study are available from the first author upon reasonable request.

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Authors and Affiliations

  1. Department of Computer Engineering and Applications, GLA University, Mathura, 281406, India

    Law Kumar Singh

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

    Munish Khanna

  3. Department of Information Technology, Hindustan College of Science and Technology, Mathura, 281122, India

    Shankar Thawkar

  4. Department of Physics, Uttar Pradesh Rajarshi Tandon Open University, Prayagraj, Uttar Pradesh, 211013, India

    Rekha Singh

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  1. Law Kumar Singh
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Singh, L.K., Khanna, M., Thawkar, S. et al. Nature-inspired computing and machine learning based classification approach for glaucoma in retinal fundus images. Multimed Tools Appl 82, 42851–42899 (2023). https://doi.org/10.1007/s11042-023-15175-6

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  • DOI: https://doi.org/10.1007/s11042-023-15175-6

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