Brain Tumor Feature Extraction from Image Analytics Using Intelligent Techniques
Feature, Brain tumor, Classification, Magnetic Resonance Image, Machine Learning, Image analytics
Image analytics is the specification of relationship between variables, features and time stamped values. It needs the extraction of large amount of feature from images and it is a kind of special task in machine learning algorithm with appropriate preprocessing to dataset.For example, Medical Image analytics is one of the most important challenging tasks nowadays. The content based medical image retrieval (CBMIR) system is used to analyze the large volume of images. This existing method is used to classify the semantic interface between low level (Cerebrum) and high level (Cerebellum) feature extraction for brain tumor in Magnetic Resonance Image (MRI) by imaging device or normal human. However, most of the existing classifications techniques are extracting and detecting the brain tumor in medical image analytics, but the performance of existing methods are very less accurate and it was more tedious and time consuming task. To overcome the above problem, using Hybrid Intelligent technique (HIT) is proposed for feature extraction and brain tumor classification in medical image analytics. The proposed technique will evaluate and validate for performance of MRI brain images and also improve overall processing speed.
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Unique Paper ID: 155369

Publication Volume & Issue: Volume 8, Issue 10

Page(s): 12 - 19
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