Severity analysis of diabetic retinopathy in retinal images using hybrid structure descriptor and modified CNNs

Abstract Imaging which plays a central role in the diagnosis and treatment planning of diabetic retinopathy and severity is an important diagnostic indicator in treatment planning and results assessment. Retinal image classification is an increasing attention among researchers in the field of computer vision, as it plays an important role in disease diagnosis. Computer Aided Diagnosis (CAD) is in wide practice in clinical work for the location and anticipation of different kinds of variations; the automated image classification systems used for such applications must be significantly efficient in terms of accuracy since false detection may lead to fatal results. Another requirement is the high convergence rate which accounts for the practical feasibility of the system. The overall classification accuracy of the proposed HTF with MCNNs is 98.41%, but the existing methods HTF with SVM and HTF with CNNs produce 97.84% and 96.65% respectively.

[1]  A. Jayachandran,et al.  Abnormality Segmentation and Classification of Multi-class Brain Tumor in MR Images Using Fuzzy Logic-Based Hybrid Kernel SVM , 2015, International Journal of Fuzzy Systems.

[2]  Bálint Antal,et al.  Improving microaneurysm detection using an optimally selected subset of candidate extractors and preprocessing methods , 2012, Pattern Recognit..

[3]  U. Rajendra Acharya,et al.  Application of Higher Order Spectra for the Identification of Diabetes Retinopathy Stages , 2008, Journal of Medical Systems.

[4]  Lei Zhang,et al.  Image retrieval based on micro-structure descriptor , 2011, Pattern Recognit..

[5]  B. Thomas,et al.  Automated identification of diabetic retinal exudates in digital colour images , 2003, The British journal of ophthalmology.

[6]  Jirí Jan,et al.  Retinal image analysis aimed at blood vessel tree segmentation and early detection of neural-layer deterioration , 2012, Comput. Medical Imaging Graph..

[7]  Xingyuan Wang,et al.  A novel method for image retrieval based on structure elements' descriptor , 2013, J. Vis. Commun. Image Represent..

[8]  Liu Feng-yu,et al.  Application of Support Vector Machines on Network Abnormal Intrusion Detection , 2006 .

[9]  Lei Zhang,et al.  Contents lists available at ScienceDirect Pattern Recognition , 2022 .

[10]  C. L. Philip Chen,et al.  A Multiple-Kernel Fuzzy C-Means Algorithm for Image Segmentation , 2011, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[11]  R. Dhanasekaran,et al.  MULTI CLASS BRAIN TUMOR CLASSIFICATION OF MRI IMAGES USING HYBRID STRUCTURE DESCRIPTOR AND FUZZY LOGIC BASED RBF KERNEL SVM , 2017 .

[12]  Yen-Jen Chang,et al.  Fast color-spatial feature based image retrieval methods , 2011, Expert Syst. Appl..

[13]  R. Dhanasekaran,et al.  Brain Tumor Detection using Fuzzy Support Vector Machine Classification based on a Texton Co-occurrence Matrix , 2013 .

[14]  R. Dhanasekaran,et al.  Severity Analysis of Brain Tumor in MRI Images Using Modified Multi-texton Structure Descriptor and Kernel-SVM , 2014, Arabian Journal for Science and Engineering.

[15]  Hiroshi Fujita,et al.  Automated segmentation of optic disc region on retinal fundus photographs: Comparison of contour modeling and pixel classification methods , 2011, Comput. Methods Programs Biomed..

[16]  Jing-Yu Yang,et al.  Content-based image retrieval using color difference histogram , 2013, Pattern Recognit..

[17]  Heinrich Niemann,et al.  Automated segmentation of the optic nerve head for diagnosis of glaucoma , 2005, Medical Image Anal..

[18]  Andrew Hunter,et al.  Optic nerve head segmentation , 2004, IEEE Transactions on Medical Imaging.

[19]  Roberto Marcondes Cesar Junior,et al.  Retinal vessel segmentation using the 2-D Gabor wavelet and supervised classification , 2005, IEEE Transactions on Medical Imaging.

[20]  David A. Clausi,et al.  Designing Gabor filters for optimal texture separability , 2000, Pattern Recognit..

[21]  Ming-Syan Chen,et al.  Adaptive Color Feature Extraction Based on Image Color Distributions , 2010, IEEE Transactions on Image Processing.