Effective Analysis of Brain Tumor Using Hybrid Data Mining Techniques

Kiranmayee, B. V.

doi:10.26483/ijarcs.v8i7.4237

Cited by 4 publications

(4 citation statements)

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“…The proposed approaches' performance was evaluated using true positive rate (TPR), false positive rate (FPR), receiver operating characteristic (ROC), area, and accuracy. The accuracy of NNge, BFTree, decision tree, LADtree, and random forest, respectively, is 96.3%, 66.7%, 66.7%, 85.2%, and 96.3% [28].…”

Section: Trends In Stroke Classification Techniquesmentioning

confidence: 99%

“…A decision tree classifies its data within its category by making a prediction. It applies the concept of tree diagram where the prediction begins with predicting data from the root of the tree until the end of the node leaf [28]. As shown in Figure 5, the root will produce a branch where the branch is a choice of value for a predictor to be compared using the trained weight.…”

Section: Fig 4 K-nn Analysis Modelmentioning

confidence: 99%

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A Review on Image Classification Techniques for MRI Brain Stroke Lesion

Norhashimah Mohd Saad,

Abdul Rahim Abdullah,

Izzatul Husna Azman

et al. 2024

ARASET

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A stroke, a fatal brain disorder with systemic consequences, emphasizes the crucial need of timely treatment. Recent studies emphasize the "time is brain" notion, which states that starting therapy within six hours improves outcomes. Manual stroke diagnosis by neuroradiologists is commonplace, but subjective and time-consuming. This study examines and views methods for classifying stroke lesions, with an emphasis on Machine Learning and Deep Learning for brain scan processing. Deep Learning thrives on complicated data but necessitates many resources. Simpler architecture is desired. The study's goal is to improve stroke classification, allowing for faster, more precise medical choices and treatment. This research has the potential to lead to enhanced healthcare solutions powered by intelligent systems.

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Section: Trends In Stroke Classification Techniquesmentioning

confidence: 99%

Section: Fig 4 K-nn Analysis Modelmentioning

confidence: 99%

A Review on Image Classification Techniques for MRI Brain Stroke Lesion

Norhashimah Mohd Saad,

Abdul Rahim Abdullah,

Izzatul Husna Azman

et al. 2024

ARASET

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“…Few of the research studies are presented in the review of brain cancer with big data and data mining. Kiranmayee et al (2017) formulated a hybrid algorithm in data mining to predict the occurrence of brain tumor using normal clinical brain dataset. The brain tumor is a form of intra cranial neoplasm that has irregular growth in human brain.…”

Section: Introductionmentioning

confidence: 99%

Pragmatic Assessment of Occurrence of Brain Cancer with Incidence Levels using Collaborative Big Data Mining Techniques

Rizwan¹,

Kuthadi²,

Rajalakshmi³

2020

Int. J. Soft Comput.

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The major objective of this research is to identify the presence of brain cancer along with the incidence levels of beginning stage to advanced stage using collaborative analysis of big data and data mining techniques. The dataset collected from secondary sources had few errors and rectified using preprocessing techniques in MATLAB. Further, the testing dataset is processed with k-means algorithm to form cluster analysis and identify the presence of brain cancer in three levels of well, fair and poor levels using degree of difference between the normal and cancer cells in brain. The algorithm is modified according to the needs of the medical analysis of the current dataset. The results indicates the presence of brain cancer in various three levels under cluster values of initial stage (54%), Curable stage (38%) and incurable stage (8%), respectively. The accuracy of prediction is 93.4% and the error identification is 9.3% whereas the sensitivity and specificity accounts to 0.8 and 0.7, respectively. Hence, further analysis is conducted in tableau big data tool and the sheets with story boards are formed. This research indicates the occurrence of brain cancer is influenced by gender and age factors along with regular activities and streams. Thus, brain cancer is considered as one of the challenging prediction as the cell contains mixed patterns with variations according to gender and age of human beings.

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“…The percentage accuracy obtained from the proposed technique is 80%. Kiranmayee et al (2017) proposed an effective analysis for MRI brain tumor using hybrid data mining techniques [12]. In the proposed approach, segmentation and classification techniques were applied using Nearest Neighbor with Generalization (NNge), Best-First Decision Tree (BFTree), LADTree, and Random Forest classifiers.…”

Section: Introductionmentioning

confidence: 99%

Automated brain tumor segmentation and classification for MRI analysis system

Saad

Yaakub

Abdullah

et al. 2019

IJEECS

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<span>This paper proposed a new analysis technique of brain tumor segmentation and classification for Fluid Attenuated Inversion Recovery (FLAIR) Magnetic Resonance Images (MRI). 25 FLAIR MRI images were collected from online database of Multimodal Brain Tumor Segmentation Challenge 2015 (BRaTS’15). The analysis comprised four stages which are preprocessing, segmentation, feature extraction and classification. Fuzzy C-Means (FCM) was proposed for brain tumor segmentation. Mean, median, mode, standard deviation, area and perimeter were calculated and utilized as the features to be fed into a rule-based classifier. The segmentation performances were assessed based on Jaccard, Dice, False Positive and False Negative Rates (FPR and FNR). The results indicate that FCM offered high similarity indices which were 0.74 and 0.83 for Jaccard and Dice indices, respectively. The technique can possibly provide high accuracy and has the potential to detect and classify brain tumor from FLAIR MRI database.</span>

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Effective Analysis of Brain Tumor Using Hybrid Data Mining Techniques

Cited by 4 publications

References 0 publications

A Review on Image Classification Techniques for MRI Brain Stroke Lesion

A Review on Image Classification Techniques for MRI Brain Stroke Lesion

Pragmatic Assessment of Occurrence of Brain Cancer with Incidence Levels using Collaborative Big Data Mining Techniques

Automated brain tumor segmentation and classification for MRI analysis system

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