Blood Vessel, Optical Disk and Damage Area-Based Features for Diabetic Detection from Retinal Images

HG-SVNN: HARMONIC GENETIC-BASED SUPPORT VECTOR NEURAL NETWORK CLASSIFIER FOR THE GLAUCOMA DETECTION

Glaucoma has emerged as the one of the leading causes of blindness. Even though the diagnosis of this disease has not yet been found, the early detection can cure the glaucoma disease. Various works presented for the glaucoma detection have many disadvantages such as increased run time, complex architecture, etc., during the real-time implementations. This work introduces the glaucoma detection system based on the proposed harmonic genetic-based support vector neural network (HG-SVNN) classifier. The proposed system detects glaucoma in the database through four major steps, (1) pre-processing, (2) proposed hybrid feature extraction, (3) segmentation and (4) classification through the proposed HG-SVNN classifier. The proposed model uses both the statistical and the vessel features from the segmented and the pre-processed images to construct the feature vector. The proposed HG-SVNN classifier uses both the harmonic operator and the genetic algorithm (GA) for the neural network training. From the simulation results, it is evident that the proposed glaucoma detection system has better performance than the existing works with the values of 0.945, 0.9, 0.9333 and 0.86667 for the segmentation accuracy, accuracy, sensitivity and specificity metric.

A review on computer-aided recent developments for automatic detection of diabetic retinopathy

Diabetic retinopathy is a serious microvascular disorder that might result in loss of vision and blindness. It seriously damages the retinal blood vessels and reduces the light-sensitive inner layer of the eye. Due to the manual inspection of retinal fundus images on diabetic retinopathy to detect the morphological abnormalities in Microaneurysms (MAs), Exudates (EXs), Haemorrhages (HMs), and Inter retinal microvascular abnormalities (IRMA) is very difficult and time consuming process. In order to avoid this, the regular follow-up screening process, and early automatic Diabetic Retinopathy detection are necessary. This paper discusses various methods of analysing automatic retinopathy detection and classification of different grading based on the severity levels. In addition, retinal blood vessel detection techniques are also discussed for the ultimate detection and diagnostic procedure of proliferative diabetic retinopathy. Furthermore, the paper elaborately discussed the systematic review accessed by authors on various publicly available databases collected from different medical sources. In the survey, meta-analysis of several methods for diabetic feature extraction, segmentation and various types of classifiers have been used to evaluate the system performance metrics for the diagnosis of DR. This survey will be helpful for the technical persons and researchers who want to focus on enhancing the diagnosis of a system that would be more powerful in real life.

Holoentropy enabled-decision tree for automatic classification of diabetic retinopathy using retinal fundus images

Diabetic retinopathy (DR) is the most common diabetic eye disease. Doctors are using various test methods to detect DR. But, the availability of test methods and requirements of domain experts pose a new challenge in the automatic detection of DR. In order to fulfill this objective, a variety of algorithms has been developed in the literature. In this paper, we propose a system consisting of a novel sparking process and a holoentropy-based decision tree for automatic classification of DR images to further improve the effectiveness. The sparking process algorithm is developed for automatic segmentation of blood vessels through the estimation of optimal threshold. The holoentropy enabled decision tree is newly developed for automatic classification of retinal images into normal or abnormal using hybrid features which preserve the disease-level patterns even more than the signal level of the feature. The effectiveness of the proposed system is analyzed using standard fundus image databases DIARETDB0 and DIARETDB1 for sensitivity, specificity and accuracy. The proposed system yields sensitivity, specificity and accuracy values of 96.72%, 97.01% and 96.45%, respectively. The experimental result reveals that the proposed technique outperforms the existing algorithms.