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Automated classification of retinal images into AMD/non-AMD Class—a study using multi-threshold and Gassian-filter enhanced images

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Abstract

Eye is an essential sensory organ in human physiological system and disease in eye severely affects the vision system. Age-related-macular-degeneration (AMD) is a common eye disease in elderly people (age > 60 years) and the untreated AMD will cause severe eye illness including permanent vision loss. This research work proposes a machine-learning-system (MLS) to detect the AMD using the fundus-retinal-images. The various phases of the MLS employed in the proposed work is as follows; (i) Image thresholding based on Shannon’ Entropy and Bat Algorithm (BA + SE), (ii) Gaussian-filter (GF) based image smoothening, (iii) feature extraction, (iv) Feature selection using statistical test, and (v) classifier implementation and validation. The image features, such as the gray level co-occurrence matrix and entropies are considered to categorize the retinal image database into AMD/Non-AMD class. The performance of classifiers, such as Naïve–Bayes (NB), decision-tree (DT), K-nearest neighbors, random-forest (RF) and support-vector-machine with linear kernel (SVM) are compared and the experimental outcome confirms that, the classification accuracy of SVM is superior (> 93%) compared to other classifiers.

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Authors and Affiliations

  1. Department of Electronics and Instrumentation Engineering, St. Joseph’s College of Engineering, Chennai,, Tamilnadu, 600 119, India

    V. Rajinikanth, R. Sivakumar & N. Sri Madhava Raja

  2. Department of ECE, Karunya Institute of Technology and Sciences, Coimbatore, Tamilnadu, 641 114, India

    D. Jude Hemanth

  3. Faculty of Applied Computing and Technology, Noroff University College, 4608, Kristiansand, Norway

    Seifedine Kadry

  4. School of Computer Applications, Kalinga Institute of Industrial Technology (Deemed To Be University), Bhubaneswar, Odisha, 751024, India

    J. R. Mohanty

  5. Department of Electronics and Communication Engineering, Manakula Vinayagar Institute of Technology, Puducherry, 605 107, India

    S. Arunmozhi

  6. Graduate School, Duy Tan University, Da Nang, 550000, Vietnam

    Nguyen Gia Nhu

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  1. V. Rajinikanth
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Correspondence to Nguyen Gia Nhu.

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Rajinikanth, V., Sivakumar, R., Hemanth, D.J. et al. Automated classification of retinal images into AMD/non-AMD Class—a study using multi-threshold and Gassian-filter enhanced images. Evol. Intel. 14, 1163–1171 (2021). https://doi.org/10.1007/s12065-021-00581-2

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