title: Automatic recognition of severity level for diagnosis of diabetic retinopathy using deep visual features.
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.
authors.affiliation: College of Computer and Information Sciences, Al Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia. qaisarabbasphd@gmail.com.
authors.forename: Qaisar
authors.identifier: http://orcid.org/0000-0002-0361-1363
authors.initials: Q
authors.lastname: Abbas
authors.affiliation: Escuela Superior de Ingenieros, Universidad de Sevilla, Camino de los Descubrimientos, s/n, 41092, Seville, Spain.
authors.forename: Irene
authors.initials: I
authors.lastname: Fondon
authors.affiliation: Escuela Superior de Ingenieros, Universidad de Sevilla, Camino de los Descubrimientos, s/n, 41092, Seville, Spain.
authors.forename: Auxiliadora
authors.initials: A
authors.lastname: Sarmiento
authors.affiliation: Surgery Department and Glaucoma Unity, University Hospital Puerta del Mar, Cádiz, Spain.
authors.forename: Soledad
authors.initials: S
authors.lastname: Jiménez
authors.affiliation: Surgery Department and Glaucoma Unity, University Hospital Puerta del Mar, Cádiz, Spain.
authors.forename: Pedro
authors.initials: P
authors.lastname: Alemany
mesh_terms: D000465:Algorithms; D003930:Diabetic Retinopathy / Q000175:diagnosis* / Q000473:pathology*; D003936:Diagnosis, Computer-Assisted / Q000379:methods; D005654:Fundus Oculi; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D010363:Pattern Recognition, Automated / Q000379:methods*; D012680:Sensitivity and Specificity
keywords: Color dense SIFT features; Computer-aided diagnosis; Deep learning; Deep visual feature; Diabetic retinopathy; Gradient location-orientation histogram; Retinal fundus images