An Enhanced Feature Selection Method to Predict the Severity in Brain Tumor ijtsrd
The level of severity of brain tumor is captured through MRI and then assessed by the physician for their medical interpretation. The facts behind the MRI images are then analyzed by the physician for further medication and follow up activities. An MRI image composed of large volume of features. It has irrelevant, missing and information which is not certain. In medical data analysis, an MRI image doesn’t express facts very clearly to the physician for correct interpretation all the time. It also includes huge amount of redundant information within it. A mathematical model known as rough set theory has been applied to resolve this problem by eliminating the redundancy in medical image data. This paper uses a rough set method to find the severity level of the brain tumor of the given MRI image. Rough set feature selection algorithms are applied over the medical image data to select the prominent features. The classification accuracy of the brain tumor can be improved to a better level by using this rough set approach. The prominent features selected through this approach deliver a set of decision rules for the classification task. A search method based on the particle swarm optimization is proposed in this paper for minimizing the attribute set. This approach is compared with previously existing rough set reduction algorithm for finding the accuracy. The reducts originated from the proposed algorithm is more efficient and can generate decision rules that will better classify the tumor types. The rule based method provided by the rough set method delivers classification accuracy in higher level than other smart methods such as fuzzy rule extraction, neural networks, decision trees and Fuzzy Networks like Fuzzy Min Max Neural Networks.
by Parthiban J | Dr. B. Sathees Kumar “An Enhanced Feature Selection Method to Predict the Severity in Brain Tumor”
Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019,
URL: https://www.ijtsrd.com/papers/ijtsrd26802.pdf
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