Automatic Detection of Diabetic Retinopathy from Color Fundus Retinal Images

The influence and impact of digital images on modern society is tremendous, and image processing is now a critical component in science and technology, in which Image segmentation plays a crucial role in many medical imaging applic ations. Medical image segmentation has a vital role in diagnosis, surgical planning, navigation, and various medical evaluations. Moreover it is suitable for segmenting the blood vessel of retinal images which is used for automated screening of early diabe tic retinopathy (damage to the retina) detection caused by complications of diabetes mellitus, which can eventually lead to blindness. One of the main challenges in medical image processing is to segment the blood vessel with higher accuracy rate hence we propose a novel technique to increase the accuracy rate of segmenting the blood vessel

Concept Based Labeling of Text Documents Using Support Vector Machine

Classification plays a vital role in many information management and retrieval tasks . Text classification uses labeled training data to learn the classification system and then automatically classifies the remaining text using the lear ned system. Classification follows various techniques such as text processing, feature extraction, feature vector construction and final classification. The proposed mining model consists of sentence - based concept analysis, document - based concept analysis, corpus - based concept - analysis, and concept - based similarity measure. The proposed model can efficiently find significant matching concepts between documents, according to the semantics of their sentences. The similarity between documents is calculate d bas ed on a n similarity measure. Then we analyze the term that contributes to the sentence semantics on the sentence, document, and corpus levels rather than the traditional analysis of the document only. With the extracted feature vector for each new document, Support Vector Machine (SVM) algorithm is applied for document classification. The approach enhances the text classification accuracy