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An Improved Image Mining Technique For Brain Tumour Classification Using Efficient Classifier
P. Rajendran,M. Madheswaran +1 more
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An improved image mining technique for brain tumor classification using pruned association rule with MARI algorithm is presented in this paper and can assist the physicians for efficient classification with multiple keywords per image to improve the accuracy.Abstract:
An improved image mining technique for brain tumor classification using pruned association rule with MARI algorithm is presented in this paper. The method proposed makes use of association rule mining technique to classify the CT scan brain images into three categories namely normal, benign and malign. It combines the low level features extracted from images and high level knowledge from specialists. The developed algorithm can assist the physicians for efficient classification with multiple keywords per image to improve the accuracy. The experimental result on prediagnosed database of brain images showed 96 percent and 93 percent sensitivity and accuracy respectively.read more
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Image mining techniques: a literature survey
TL;DR: This paper presents a survey on various image mining techniques and provides a improvements for future research.
Journal ArticleDOI
Detection and Identification of Tumor Region from MRI Brain Image using Image Segmentation
TL;DR: This analysis is concentrated towards highlight the strength and limitations of earlier projected classification techniques mentioned within the up to date literature and an important analysis of the surveyed literature that reveals new sides of analysis.
Journal ArticleDOI
Early Detection of Brain Cancer in Obese and Non- Obese Patients by using Data Mining Techniques
TL;DR: The results which are concluded can be used as supporting or assistant tool for neuro-oncologist for identification and diagnosis of brain tumor by applying datamining and classification techniques.
Book ChapterDOI
A Survey of Techniques Used in Processing and Mining of Medical Images
TL;DR: This paper helps to understand the different techniques used in different phases of medical image processing and mining like pre-processing, feature extraction, segmentation, classification, indexing, storing and retrieval.
References
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Textural Features for Image Classification
TL;DR: These results indicate that the easily computable textural features based on gray-tone spatial dependancies probably have a general applicability for a wide variety of image-classification applications.
Proceedings ArticleDOI
Mining association rules between sets of items in large databases
TL;DR: An efficient algorithm is presented that generates all significant association rules between items in the database of customer transactions and incorporates buffer management and novel estimation and pruning techniques.
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Mutual-information-based registration of medical images: a survey
TL;DR: An overview is presented of the medical image processing literature on mutual-information-based registration, an introduction for those new to the field, an overview for those working in the field and a reference for those searching for literature on a specific application.
Posted Content
Estimating Continuous Distributions in Bayesian Classifiers
George H. John,Pat Langley +1 more
TL;DR: This paper abandon the normality assumption and instead use statistical methods for nonparametric density estimation for kernel estimation, which suggests that kernel estimation is a useful tool for learning Bayesian models.
Proceedings Article
Estimating continuous distributions in Bayesian classifiers
George H. John,Pat Langley +1 more
TL;DR: In this paper, the authors use statistical methods for nonparametric density estimation for a naive Bayesian classifier, comparing two methods of density estimation: assuming normality and modeling each conditional distribution with a single Gaussian; and using non-parametric kernel density estimation.
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