Improved adaptive neuro-fuzzy inference system based on modified glowworm swarm and differential evolution optimization algorithm for medical diagnosis.

Medical diagnosis has seen a tremendous advancement in the recent years due to the advent of modern and hybrid techniques that aid in screening and management of the disease. This paper figures a predictive model for detecting neurodegenerative diseases like glaucoma, Parkinson's disease and carcinogenic diseases like breast cancer. The proposed approach focuses on enhancing the efficiency of adaptive neuro-fuzzy inference system (ANFIS) using a modified glowworm swarm optimization algorithm (M-GSO). This algorithm is a global optimization wrapper approach that simulates the collective behavior of glowworms in nature during food search. However, it still suffers from being trapped in local minima. Hence in order to improve glowworm swarm optimization algorithm, differential evolution (DE) algorithm is utilized to enhance the behavior of glowworms. The proposed (DE-GSO-ANFIS) approach estimates suitable prediction parameters of ANFIS by employing DE-GSO algorithm. The outcomes of the proposed model are compared with traditional ANFIS model, genetic algorithm-ANFIS (GA-ANFIS), particle swarm optimization-ANFIS (PSO-ANFIS), lion optimization algorithm-ANFIS (LOA-ANFIS), differential evolution-ANFIS (DE-ANFIS) and glowworm swarm optimization (GSO). Experimental results depict better performance and superiority of the DE-GSO-ANFIS over the similar methods in predicting medical disorders.

State-of-the-Art Techniques in Optic Cup and Disc Localization for Glaucoma Diagnosis: Research Results and Issues.

Glaucoma is a heterogeneous group of diseases that are characterized by loss of retinal ganglion cells, which damages the optic nerve head (ONH) and visual field. If glaucoma, the most frequent cause of irretrievable vision loss, is detected at an initial stage, the rate of blindness may be reduced by nearly 50%-55%. Manual diagnosis is a laborious task; it is fairly time consuming and requires a skilled medical provider. With the lack of trained professionals in developing countries, automatic glaucoma diagnosis becomes an increasingly vital tool that aids in detection and disease risk analysis. Analyses of the optic disc (OD) and optic cup (OC) are normally performed to assess ONH damage. But of the numerous reported research reports that show results using machine-learning and image-processing approaches, major concern lies in the accuracy of segmenting and classifying OD and OC. The objective of the current study is to outline state-of-the-art image-processing techniques that are used to detect glaucoma early via segmenting and OD and OC classification. We also present research findings and limitations thereof that must be addressed to achieve higher accuracy to improve segmentation and classification quality.

Glaucoma classification based on intra-class and extra-class discriminative correlation and consensus ensemble classifier.

Automatic classification of glaucoma from fundus images is a vital diagnostic tool for Computer-Aided Diagnosis System (CAD). In this work, a novel fused feature extraction technique and ensemble classifier fusion is proposed for diagnosis of glaucoma. The proposed method comprises of three stages. Initially, the fundus images are subjected to preprocessing followed by feature extraction and feature fusion by Intra-Class and Extra-Class Discriminative Correlation Analysis (IEDCA). The feature fusion approach eliminates between-class correlation while retaining sufficient Feature Dimension (FD) for Correlation Analysis (CA). The fused features are then fed to the classifiers namely Support Vector Machine (SVM), Random Forest (RF) and K-Nearest Neighbor (KNN) for classification individually. Finally, Classifier fusion is also designed which combines the decision of the ensemble of classifiers based on Consensus-based Combining Method (CCM). CCM based Classifier fusion adjusts the weights iteratively after comparing the outputs of all the classifiers. The proposed fusion classifier provides a better improvement in accuracy and convergence when compared to the individual algorithms. A classification accuracy of 99.2% is accomplished by the two-level hybrid fusion approach. The method is evaluated on the public datasets High Resolution Fundus (HRF) and DRIVE datasets with cross dataset validation.

Analysis of hybrid statistical textural and intensity features to discriminate retinal abnormalities through classifiers.

Retinal image analysis relies on the effectiveness of computational techniques to discriminate various abnormalities in the eye like diabetic retinopathy, macular degeneration and glaucoma. The onset of the disease is often unnoticed in case of glaucoma, the effect of which is felt only at a later stage. Diagnosis of such degenerative diseases warrants early diagnosis and treatment. In this work, performance of statistical and textural features in retinal vessel segmentation is evaluated through classifiers like extreme learning machine, support vector machine and Random Forest. The fundus images are initially preprocessed for any noise reduction, image enhancement and contrast adjustment. The two-dimensional Gabor Wavelets and Partition Clustering is employed on the preprocessed image to extract the blood vessels. Finally, the combined hybrid features comprising statistical textural, intensity and vessel morphological features, extracted from the image, are used to detect glaucomatous abnormality through the classifiers. A crisp decision can be taken depending on the classifying rates of the classifiers. Public databases RIM-ONE and high-resolution fundus and local datasets are used for evaluation with threefold cross validation. The evaluation is based on performance metrics through accuracy, sensitivity and specificity. The evaluation of hybrid features obtained an overall accuracy of 97% when tested using classifiers. The support vector machine classifier is able to achieve an accuracy of 93.33% on high-resolution fundus, 93.8% on RIM-ONE dataset and 95.3% on local dataset. For extreme learning machine classifier, the accuracy is 95.1% on high-resolution fundus, 97.8% on RIM-ONE and 96.8% on local dataset. An accuracy of 94.5% on high-resolution fundus 92.5% on RIM-ONE and 94.2% on local dataset is obtained for the random forest classifier. Validation of the experiment results indicate that the hybrid features can be deployed in supervised classifiers to discriminate retinal abnormalities effectively.