Performance analysis of screening diabetic retinopathy

804-809This study presents a new method for screening Diabetic Retinopathy (DR), is the leading ophthalmic pathological cause of blindness among people of working age in developed countries. The first manifestations of DR are tiny capillary dilations known as Microaneurysms (MA) and Exudates. It may provide an early indication of the risk of the Type –I Diabetes. The various features of the images of the Retinal Vessels are used to indicate the different MA’s and Exudates disease processes. Neural Networks and k-means clustering provide significant benefits in medical research. This Proposed work deals DR with Segmentation and Classification algorithms for the analysis of Retina images. This effectiveness and robustness, together with its simplicity make this Optimized analysis for being integrated into a complete screening system for early DR detection. It proposes an Optimized Soft Computing technique approach for screening the Diabetic retinopathy

Diagnosis System for Diabetic Retinopathy and Glaucoma Screening to Prevent Vision Loss

Aim: Diabetic retinopathy (DR) and glaucoma are two most common retinal disorders that are major causes of blindness in diabetic patients. DR caused in retinal images due to the damage in retinal blood vessels, which leads to the formation of hemorrhages spread over the entire region of retina. Glaucoma is caused due to hypertension in diabetic patients. Both DR and glaucoma affects the vision loss in diabetic patients. Hence, a computer aided development of diagnosis system for Diabetic retinopathy and Glaucoma screening is proposed in this paper to prevent vision loss. Method: The diagnosis system of DR consists of two stages namely detection and segmentation of fovea and hemorrhages. The diagnosis system of glaucoma screening consists of three stages namely blood vessel segmentation, Extraction of optic disc (OD) and optic cup (OC) region and determination of rim area between OD and OC. Results: The specificity and accuracy for hemorrhages detection is found to be 98.47% and 98.09% respectively. The accuracy for OD detection is found to be 99.3%. This outperforms state-of-the-art methods. Conclusion: In this paper, the diagnosis system is developed to classify the DR and glaucoma screening in to mild, moderate and severe respectively

Screening Diabetic Retinopathy in Developing Countries using Retinal Images

In developing countries, diabetic retinopathy (DR) is the leading cause of blindness in diabetic patients due to intraocular hypertension or high glucose level. Its detection in an earlier stage is essential to prevent vision loss in type 2 diabetic patients. In this paper, the computer aided automatic screening system for diabetic retinopathy is proposed. DR can be diagnosed by detecting the abnormal lesions such as hemorrhages in retinal images and analyzing its relationship with the fovea region. The proposed method consists of the following stages, namely: retinal image enhancement and classification, hemorrhages detection and segmentation, fovea localization and Diabetic Retinopathy classification. The multi directional local histogram equalization is used to enhance the retinal image for better classification rate. The Gabor transform and Support vector machine (SVM) classifier is used for retinal image classifications. The proposed method is tested on publicly available HRFand DIARETDB1datasets. The sensitivity and specificity of hemorrhages detection are 94.76% and 99.85%, respectively. Thus, the severity of Diabetic Retinopathy in Type 2 diabetic patients can be easily identified by detecting fovea region and hemorrhage lesions and analyzing the relation between them to prevent vision loss in diabetic patients

Meningioma brain tumor detection and classification using hybrid CNN method and RIDGELET transform

Abstract The detection of meningioma tumors is the most crucial task compared with other tumors because of their lower pixel intensity. Modern medical platforms require a fully automated system for meningioma detection. Hence, this study proposes a novel and highly efficient hybrid Convolutional neural network (HCNN) classifier to distinguish meningioma brain images from non-meningioma brain images. The HCNN classification technique consists of the Ridgelet transform, feature computations, classifier module, and segmentation algorithm. Pixel stability during the decomposition process was improved by the Ridgelet transform, and the features were computed from the coefficient of the Ridgelet. These features were classified using the HCNN classification approach, and tumor pixels were detected using the segmentation algorithm. The experimental results were analyzed for meningioma tumor images by applying the proposed method to the BRATS 2019 and Nanfang dataset. The proposed HCNN-based meningioma detection system achieved 99.31% sensitivity, 99.37% specificity, and 99.24% segmentation accuracy for the BRATS 2019 dataset. The proposed HCNN technique achieved99.35% sensitivity, 99.22% specificity, and 99.04% segmentation accuracy on brain Magnetic Resonance Imaging (MRI) in the Nanfang dataset. The proposed system obtains 99.81% classification accuracy, 99.2% sensitivity, 99.7% specificity and 99.8% segmentation accuracy on BRATS 2022 dataset. The experimental results of the proposed HCNN algorithm were compared with those of the state-of-the-art meningioma detection algorithms in this study