Diabetic Retinopathy that is DR which is a eye disease that affect retina and further later at severe stage it lead to vision loss. Early detection of DR is helpful to improve the screening of patient to prevent further damage. Retinal micro-aneurysms, haemorrhages, exudates and cotton wool spots are kind of major abnormality to find the Non-Proliferative Diabetic Retinopathy (NPDR) and Proliferative Diabetic Retinopathy (PDR). The main objective of our proposed work is to detect retinal micro aneurysms and exudates for automatic screening of DR using Support Vector Machine (SVM) and KNN classifier. To develop this proposed system, a detection of red and bright lesions in digital fundus photographs is needed. Micro-aneurysms are the first clinical sign of DR and it appear small red dots on retinal fundus images. To detect retinal micro-aneurysms, retinal fundus images are taken from Messidor, DB-reet dataset. After pre-processing, morphological operations are performed to find micro-aneurysms and then features are get extracted such as GLCM and Structural features for classification. In order to classify the normal and DR images, different classes must be represented using relevant and significant features. SVM gives better performance over KNN classifier.
Traditional video retrieval methods fail to meet technical challenges due to large and rapid growth of multimedia data, demanding effective retrieval systems. In this paper we propose integration of entropy and black and white points on edge, features of video key frames for developing proposed video retrieval systems. First, video feature database is created using entropy feature extracted from key video frames of video database. Same feature is extracted from video frame query. We then extract the edge and black and white points on edge from database frames and query frame. Finally similarity measure is applied to retrieve the best matching frames and corresponding videos are presented as output. The experimental results show that feature integration is effective.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.