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

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 extensi...

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Published in:Medical & Biological Engineering & Computing Vol. 55; no. 11; pp. 1959 - 1975
Main Authors: Abbas, Qaisar, Fondon, Irene, Sarmiento, Auxiliadora, Jiménez, Soledad, Alemany, Pedro
Format: Journal Article
Published: Springer Nature Nov2017
Online Access:View this record in EBSCOhost
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      jtl: Medical & Biological Engineering & Computing
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      dt: Nov2017
      vid: 55
      iid: 11
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      pub: Springer Nature
      place: New York, New York
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        10.1007/s11517-017-1638-6
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      tig:
        atl: Automatic recognition of severity level for diagnosis of diabetic retinopathy using deep visual features.
      aug:
        au:
          Abbas, Qaisar
          Fondon, Irene
          Sarmiento, Auxiliadora
          Jiménez, Soledad
          Alemany, Pedro
        affil: College of Computer and Information Sciences , Al Imam Mohammad Ibn Saud Islamic University (IMSIU) , Riyadh Saudi Arabia
      sug:
        subj:
          Diabetic Retinopathy Diagnosis
          Information Science Methods
          Diabetic Retinopathy Pathology
          Algorithms
          Diagnosis, Computer Assisted Methods
          Retina
          Sensitivity and Specificity
          Image Interpretation, Computer Assisted Methods
      ab: 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.
      pubtype: Academic Journal
      doctype: Journal Article
      ougenre: Article
    language: English
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