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Multi-retinal disease classification by reduced deep learning features

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Abstract

This paper presents the retina-based disease diagnosis through deep learning-based feature extraction method. This process helps in developing automated screening system, which is capable of diagnosing retina for diseases such as age-related molecular degeneration, diabetic retinopathy, macular bunker, retinoblastoma, retinal detachment, and retinitis pigmentosa. Some of these diseases share a common characteristic, which makes the classification difficult. In order to overcome the above-mentioned problem, deep learning feature extraction and a multi-class SVM classifier are used. The main contribution of this work is the reducing the dimension of the features required to classify the retinal disease, which enhances the process of reducing the system requirement as well as good performance.

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Authors and Affiliations

  1. Department of Electronics and Communication Engineering, Karpagam College of Engineering, Coimbatore, India

    R. Arunkumar

  2. Electronics and Telecommunication Engineering, Karpagam College of Engineering, Coimbatore, India

    P. Karthigaikumar

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  1. R. Arunkumar
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  2. P. Karthigaikumar
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Correspondence to P. Karthigaikumar.

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Arunkumar, R., Karthigaikumar, P. Multi-retinal disease classification by reduced deep learning features. Neural Comput & Applic 28, 329–334 (2017). https://doi.org/10.1007/s00521-015-2059-9

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  • DOI: https://doi.org/10.1007/s00521-015-2059-9

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