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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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Big data analytics for preventive medicine
TL;DR: This review introduces disease prevention and its challenges followed by traditional prevention methodologies, and summarizes state-of-the-art data analytics algorithms used for classification of disease, clustering, anomalies detection, and association as well as their respective advantages, drawbacks and guidelines.
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Multimedia data mining: state of the art and challenges
TL;DR: A survey on the problems and solutions in Multimedia Data Mining, approached from the following angles: feature extraction, transformation and representation techniques, data mining techniques, and current multimedia data mining systems in various application domains.
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Wavelet statistical texture features-based segmentation and classification of brain computed tomography images
A. Padma Nanthagopal,R. Sukanesh +1 more
TL;DR: The experimental results show that the proposed SVM classifier is able to achieve high segmentation and classification accuracy effectiveness as measured by sensitivity and specificity.
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Applications of association rule mining in health informatics: a survey
TL;DR: It has been explored that, instead of the more efficient alternative approaches, the Apriori algorithm is still a widely used frequent itemset generation technique for application of association rule mining for health informatics.
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Investigating machine learning techniques for MRI-based classification of brain neoplasms.
TL;DR: A computer-assisted classification framework is developed and used for differential diagnosis of brain neoplasms based on MRI that can achieve higher accuracy than most reported studies using MRI.
References
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Proceedings ArticleDOI
Retrieval by content of medical images using texture for tissue identification
TL;DR: A tool for obtaining the relevant textures from images was implemented and the accuracy degree obtained was always over 90% for queries asking for similar images for up to 20% of the database.
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Data mining in brain imaging
TL;DR: This work presents data mining methods that have been or could be employed in the analysis of brain images, and introduces statistical methods that aid the discovery of interesting associations and patterns between brain images and other clinical data.
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Application of the Naïve Bayesian Classifier to optimize treatment decisions
Joanna Kazmierska,Julian Malicki +1 more
TL;DR: Naïve Bayesian Classifier is a useful tool to support the assessment of individual risk of relapse or progression in patients diagnosed with brain tumour undergoing RT postoperatively.
Journal Article
Associative classifiers for medical images
TL;DR: This paper presents two classification systems for medical images based on association rule mining, and illustrates how important the data cleaning phase is in building an accurate data mining architecture for image classification.
Journal ArticleDOI
An Association Rule-Based Method to Support Medical Image Diagnosis With Efficiency
TL;DR: A method based on association rule-mining to enhance the diagnosis of medical images (mammograms) and claims that the use of association rules is a powerful means to assist in the diagnosing task.
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