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Novel angular binary pattern (NABP) and kernel based convolutional neural networks classifier for cataract detection

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Abstract

Cataract is considered as one of the foremost causes of blindness, especially among older people. In India, by the age 80, nearly half of older population either have cataract or they performed surgery for it. To avoid worse effects in eyesight like complete blindness or blurred vision, it is essential that cataract cases are detected in the initial stages for effective treatment. For detecting eye cataracts, the machines utilizing are exhibiting portability issues. Hence, this study is using digital image processing algorithms for the detection and classification of cataract on eye images along with its severity. Initially, the features such as color, shape and texture are extracted separately. Significantly, Novel Angular Binary Pattern- NABP is proposed for the texture feature extraction. The classification of images are performed in this study using the proposed Kernel Based Convolutional Neural Network after the feature extraction process. For all three feature types, the results are obtained separately. Performance of the proposed system is comparatively analyzed in terms of Accuracy, Sensitivity, Specificity, Precision, Recall and F – measure. In addition, comparative analysis is undertaken with respect to texture, colour and shape features. Classification results of the proposed system for varying epochs are also analyzed. Thus, all the analytical results confirmed the outstanding performance of the proposed system than conventional systems for cataract detection with 97.3% accuracy.

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Funding

This research work was not funded by any organization/institute/agency.

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Authors and Affiliations

  1. Department of Electronics & Communication Engineering, Aurora’s Scientific and Technological Institute, Aushapur, Ghatkesar, Telangana, India

    A. Sirajudeen

  2. Department of ECE, Sri Eshwar College of Engineering, Coimbatore, Tamil Nadu, India

    Anuradha Balasubramaniam

  3. Department of ECE, Chaitanya Bharathi Institute of Technology, Proddatur, Andhra Pradesh, India

    S. Karthikeyan

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  3. S. Karthikeyan
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Contributions

I Am Dr. A. Sirajudeen Hereby State That The Manuscript Title Entitled “Detection of Cataract through feature extraction by the Novel Angular Binary Pattern (NABP) and classification by Kernel Based Convolutional Neural Networks” Submitted To The Multimedia tools and applications, I Confirm That This Work Is Original And Has Not Been Published Elsewhere, Nor Is It Currently Under Consideration For Publication Elsewhere. And I Am Professor In the Department of ECE, Aurora’s Scientific & Technological Institute, Aushapur, Ghatkesar, Telangana.

I’m the corresponding author of our paper, my contribution work on this paper is to Writing, developing, and reviewing the content of the manuscript. And my co-author Anuradha balasubramaniam, and Dr.S.Karthikeyan works were to cite the figure, table and references. Equally I have done 35% and my second author has done 35% of the work and my third author have done 30%. We are the entire contributors of our paper. And no other third party people are not involved in this paper.

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Correspondence to A. Sirajudeen.

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Sirajudeen, A., Balasubramaniam, A. & Karthikeyan, S. Novel angular binary pattern (NABP) and kernel based convolutional neural networks classifier for cataract detection. Multimed Tools Appl 81, 38485–38512 (2022). https://doi.org/10.1007/s11042-022-13092-8

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