Non-Calcified Plaque Detection using SVM
Prevalence and Prognostic Implication of Non-Calcified Plaque in Asymptomatic Population with Coronary Artery Calcium Score of Zero
... scribed in previous manuscript. 21) Coronary artery calcium scores were measured using the scoring system previously suggested by Agatston et al. 22) The CCTA was performed on a 64-slice MDCT scanner (Brilliance ...
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Framework for Detection and Localization of Coronary Non-Calcified Plaques in Cardiac CTA using Mean Radial Profiles
... represented using 20 subsets. non-calcified plaques can be optimally detected by investigating the composition of the arterial ...abnormality detection process. Moreover, ...
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FACE DETECTION AND IDENTIFICATION USING SVM
... categories. SVM models are closely related to neural networks. In fact, a SVM model using a sigmoid kernel function is equivalent to a two-layer, perceptron neural ...networks. Using a kernel ...
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AN EFFICIENT HEMORRHAGE DETECTION USING SVM CLASSIFIER
... distributions of hemorrhage pixels and non- hemorrhage pixels are imbalanced, since hemorrhages usually account for a small fraction of the entire image. To create splats which preserve desired boundaries ...
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Abandoned Object Detection using SVM Classifier
... Filter is used to remove noise from the gray scale image. Median filter is used to remove unwanted information, somewhat like mean filter. However it often does a better job than mean filter. This class of filter belongs ...
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Lung Cancer Detection and Classification Using SVM
... 4.4.1 Phase of training LIDC information base is utilized in the preparation stage. The information base subset, which is shaped arbitrarily comprises of 271 CT lung picture filters. The gray scale pictures are in DICOM ...
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Detection, localization and quantification of non-calcified coronary plaques in contrast enhanced CT angiography
... segmentations using localized intensity ...darker non-calcified plaque ...soft plaque voxels from the boundary during ...into plaque voxels when driven by localized ...
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Fake Currency Detection using Clustering and SVM Classification
... an SVM training algorithm builds a model that assigns new examples to one category or the other, making it a non-probabilistic binary linear classifier (although methods such as Platt scaling exist to use ...
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Fruit Disease Detection Using GLCM And SVM Classifier
... FEATURE EXTRACTION Feature extraction is a dimensionality reduction process which plays a very important role in the area of image processing. Initially, various image pre- processing techniques like binarization, ...
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Detection Of Pharming Attack On Websites Using Svm Classifier
... Generally non-numeric features are omitted from dataset as they do not really play an important part in detecting any malicious ...eliminate non-numeric and irrelevant symbols to improve the accuracy of ...
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Face Detection using SVM Trained in Eigenfaces Space
... The set wm contained 300 images of 30 different individuals recorded in 3 sessions (10 individuals per session). For training, the positive example set consisted always of 684 images from wm and either g1 or g2 (2 images ...
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Automatic Rice Leaf Diseases Detection Using SVM
... Classification using SVM Support vector machine (SVM) is a powerful supervised classifier and accurate learning ...diagnosis. SVM is based on the structural risk minimization principle from ...
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Plant Leaf Disease Detection using SVM IWD Approach
... But various factors are there that can destroy plant growth like weather conditions, non-availability of accurate resources, plant diseases and lack of expert knowledge to care plants. Plant diseases are one of ...
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Brain Tumor Detection using K Mean Clustering and SVM
... or non-cancerous mass or growth of abnormal cells in the ...done using x-rays, strong magnets, or radioactive substances to create pictures of the ...diagnosed using different types of ...
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Community Kernels Detection in OSN using SVM Clustering and Classification
... of non-structured, semi-structured and structured data based on broadband network, carry on security assurance related characteristic selections and topic identification, perform social network analysis of ...
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Automatic Vehicle Detection and Tracking in Aerial Surveillances using SVM
... vehicle detection system for aerial surveillance background colors are eliminated and then features are ...and non-vehicle colors effectively. For edges detection, system applies moment-preserving ...
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Lung Cancer Detection using SVM Classifier and MFPCM Segmentation
... and non-occurrence of cancer nodule for the supplied lung ...features. SVM is a binary classification method that takes as input labelled data from two classes and outputs a model file for classifying new ...
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Road Sign Detection and Recognition System Using LBP and SVM
... standard SVM is a supervised binary classifier based on statistical and optimizing theories, which has found widespread use in pattern recognition ...The SVM is able to handle noise, large data set, input ...
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DETECTION OF SKIN CANCER USING HYBRID OF SVM-ID3 ALGORITHM
... 2. IMAGE BACKGROUND Almost all the parts of our body is completely covered by skin, to precisely diagnose the skin diseases, biopsy is the most common method used today. But, it is invasive, painful to the patient and ...
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Driver Fatigue Detection using Mean Intensity, SVM, and SIFT
... of non-invasive methods, such as making a video of the driver and alerting him/her on using cues that may help in anticipating the presence of a sleep pattern, can be a useful way to detect driver fatigue ...
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