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Automatic recognition of severity level for diagnosis of diabetic retinopathy using deep visual features

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Abstract

Diabetic retinopathy (DR) is leading cause of blindness among diabetic patients. Recognition of severity level is required by ophthalmologists to early detect and diagnose the DR. However, it is a challenging task for both medical experts and computer-aided diagnosis systems due to requiring extensive domain expert knowledge. In this article, a novel automatic recognition system for the five severity level of diabetic retinopathy (SLDR) is developed without performing any pre- and post-processing steps on retinal fundus images through learning of deep visual features (DVFs). These DVF features are extracted from each image by using color dense in scale-invariant and gradient location-orientation histogram techniques. To learn these DVF features, a semi-supervised multilayer deep-learning algorithm is utilized along with a new compressed layer and fine-tuning steps. This SLDR system was evaluated and compared with state-of-the-art techniques using the measures of sensitivity (SE), specificity (SP) and area under the receiving operating curves (AUC). On 750 fundus images (150 per category), the SE of 92.18%, SP of 94.50% and AUC of 0.924 values were obtained on average. These results demonstrate that the SLDR system is appropriate for early detection of DR and provide an effective treatment for prediction type of diabetes.

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Acknowledgements

This research was supported by the Research and Development Program of Al Imam Mohammad Ibn Saud Islamic University (IMSIU), Saudi Arabia (Grant No. 2006AA02Z347). We would also like to thank two expert ophthalmologists for providing us new private dataset.

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Authors and Affiliations

  1. College of Computer and Information Sciences, Al Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia

    Qaisar Abbas

  2. Escuela Superior de Ingenieros, Universidad de Sevilla, Camino de los Descubrimientos, s/n, 41092, Seville, Spain

    Irene Fondon & Auxiliadora Sarmiento

  3. Surgery Department and Glaucoma Unity, University Hospital Puerta del Mar, Cádiz, Spain

    Soledad Jiménez & Pedro Alemany

Authors
  1. Qaisar Abbas
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  2. Irene Fondon
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  3. Auxiliadora Sarmiento
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  4. Soledad Jiménez
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  5. Pedro Alemany
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Correspondence to Qaisar Abbas.

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Abbas, Q., Fondon, I., Sarmiento, A. et al. Automatic recognition of severity level for diagnosis of diabetic retinopathy using deep visual features. Med Biol Eng Comput 55, 1959–1974 (2017). https://doi.org/10.1007/s11517-017-1638-6

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  • DOI: https://doi.org/10.1007/s11517-017-1638-6

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