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International Conference on Recent Trends in Image Processing and Pattern Recognition

RTIP2R 2018: Recent Trends in Image Processing and Pattern Recognition pp 174–188Cite as

DRAODM: Diabetic Retinopathy Analysis Through Optimized Deep Learning with Multi Support Vector Machine for Classification

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Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1036))

Abstract

Diabetic retinopathy (DR) is the leading cause of avertable blindness globally. Retinal scanning of eyes is critical for examining the disease at an early stage. The concern of this study is to develop a robust mechanism to automate the process of diabetic retinopathy detection. A person suffering from DR must be referred to an ophthalmologist for an early evaluation in order to reduce the rate of vision loss hence enabling early treatment and prevention of vision loss. The proposed methodology introduces a data-driven novel algorithm using Deep learning for creating a tool for detecting DR. The approach uses DR colored fundus images and classifies them into multiple classes as stages or levels to which the eye is infected. Set of 170 colored fundus images of diabetic patients were used to train and test the model for distinguishing images into multiple classes. Entire simulation is divided into primarily two phases-firstly, the preprocessing phase where the resizing operation is performed since the input layer of the network requires predefined size. Size of 77 × 100 and channel of size 3 is used, indicating RGB image. Gaussian Filtering is used to tackle noise (De-noising) if any within the image. Secondly, the MSVM for segmentation and Classification phase where Multi-class SVM is used to extract critical and non-critical segments from within the training images. After extracting the features, classification is performed on the test images. Parameters used for optimization includes Accuracy, sensitivity, and specificity. Simulation is conducted in MATLAB using image processing and neural network toolbox. The proposed mechanism shows improvement in terms of classification accuracy by the margin of 5–6% which is significant, thereby enhancing the recognition rate.

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Authors and Affiliations

  1. Computer Engineering and Technology, Guru Nanak Dev University, Amritsar, India

    Emmy Bhatti & Prabhpreet Kaur

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  1. Emmy Bhatti
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  2. Prabhpreet Kaur
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Correspondence to Emmy Bhatti .

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Editors and Affiliations

  1. Department of Computer Science, University of South Dakota, Vermillion, SD, USA

    K. C. Santosh

  2. Solapur University, Solapur, India

    Ravindra S. Hegadi

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Bhatti, E., Kaur, P. (2019). DRAODM: Diabetic Retinopathy Analysis Through Optimized Deep Learning with Multi Support Vector Machine for Classification. In: Santosh, K., Hegadi, R. (eds) Recent Trends in Image Processing and Pattern Recognition. RTIP2R 2018. Communications in Computer and Information Science, vol 1036. Springer, Singapore. https://doi.org/10.1007/978-981-13-9184-2_16

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  • DOI: https://doi.org/10.1007/978-981-13-9184-2_16

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-9183-5

  • Online ISBN: 978-981-13-9184-2

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