Classification of Pathological Magnetic Resonance Images of Brain Using Data Mining Techniques

Ramani, R.; Sivaselvi, K.

doi:10.1109/icrtccm.2017.48

Cited by 6 publications

(4 citation statements)

References 14 publications

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“…4 Results discussion Our suggested method has been evaluated with traditional SVM+RBF and Decision Tree Algorithm. The decision tree classifier is a tree in which the internal nodes represent the features; the edges left out of the nodes are criteria for selecting the attributes, and the leaves representing the categories [Ramani and Sivaselvi (2017)]. To implement the SSA based feature selection, the objective function of the SSA algorithm has been set up to respectively figure out the optimal C and γ.…”

Section: Classification In Proposed Methodsmentioning

confidence: 99%

A Novel Image Categorization Strategy Based on Salp Swarm Algorithm to Enhance Efficiency of MRI Images

Nejad¹,

Ahmadabadi²

2019

Computer Modeling in Engineering &Amp; Sciences

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The main target of this paper is presentation of an efficient method for MRI images classification so that it can be used to diagnose patients and non-patients. Image classification is one of the prominent subset topics of machine learning and data mining that the most important image technique is the auto-categorization of images. MRI images with high resolution and appropriate accuracy allow physicians to decide on the diagnosis of various diseases and treat them. The auto categorization of MRI images toward diagnosing brain diseases has been being used to accurately diagnose hospitals, clinics, physicians and medical research centers. In this paper, an effective method is proposed for categorizing MRI images, which emphasizes the classification stage. In this method, images have been firstly collected and tagged, and then the discrete wavelet transform method has been implemented to extract the relevant properties. All the ready features in a matrix will be subsequently held, and PCA method has been applied to reduce the features dimension. Furthermore, a new model using support vector machine classifier with radial basis function kernel i.e. SVM+RBF has been performed. The SVM Algorithm must bimanually initialized, while, these values have been automatically entered into the SVM classifier by Salp Swarm Algorithm (SSA): Due to high performance of SSA in fast and accurate solution of nonlinear problem as compared to other optimization algorithms, it has been applied to optimally solve the designed problem. Finally, after applying the optimal parameters and SVM classification training, the test data has been utilized and evaluated. The results have transparently suggested the effectiveness of the proposed method in the Accuracy criteria with 0.9833, the Sensitivity with 1, Specificity with 0.9818 and Error with 0.0167 in best iteration as compared to the conventional SVM method.

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Section: Classification In Proposed Methodsmentioning

confidence: 99%

A Novel Image Categorization Strategy Based on Salp Swarm Algorithm to Enhance Efficiency of MRI Images

Nejad¹,

Ahmadabadi²

2019

Computer Modeling in Engineering &Amp; Sciences

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“…The author used machine learning algorithms with clustering, classification and association methods to interpret, predict and analyze the results. Ramani and Sivaselvi (2017) endeavored to conduct an analysis on the performace of various supervised algorithms in data mining to classify brain Magnetic Resonance images. The images collected are preprocessed to avoid any damages and then involved with feature selection technique like analysis, fisher filtering and relief feature selection to determine best features.…”

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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“…Ramani R.G. et al [8] proposed Naive Bayes, Support Vector Machine and random tree to analyze the machine learning technique in classifying normal and abnormal brain image. Mudali D. et al [9] proposed decision tree method to interpret the diagnosis of neurodegenerative brain diseases.…”

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confidence: 99%

Automated segmentation and classification technique for brain stroke

Noor¹,

Saad²,

Abdullah³

et al. 2019

IJECE

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Difussion-Weighted Imaging (DWI) plays an important role in the diagnosis of brain stroke by providing detailed information regarding the soft tissue contrast in the brain organ. Conventionally, the differential diagnosis of brain stroke lesions is performed manually by professional neuroradiologists during a highly subjective and time- consuming process. This study proposes a segmentation and classification technique to detect brain stroke lesions based on diffusion-weighted imaging (DWI). The type of stroke lesions consists of acute ischemic, sub-acute ischemic, chronic ischemic and acute hemorrhage. For segmentation, fuzzy c-Means (FCM) and active contour is proposed to segment the lesion’s region. FCM is implemented with active contour to separate the cerebral spinal fluid (CSF) with the hypointense lesion. Pre-processing is applied to the DWI for image normalization, background removal and image enhancement. The algorithm performance has been evaluated using Jaccard Index, Dice Coefficient (DC) and both false positive rate (FPR) and false negative rate (FNR). The average results for the Jaccard index, DC, FPR and FNR are 0.55, 0.68, 0.23 and 0.23, respectively. First statistical order method is applied to the segmentation result to obtain the features for the classifier input. For classification technique, bagged tree classifier is proposed to classify the type of stroke. The accuracy results for the classification is 90.8%. Based on the results, the proposed technique has potential to segment and classify brain stroke lesion from DWI images.

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Classification of Pathological Magnetic Resonance Images of Brain Using Data Mining Techniques

Cited by 6 publications

References 14 publications

A Novel Image Categorization Strategy Based on Salp Swarm Algorithm to Enhance Efficiency of MRI Images

A Novel Image Categorization Strategy Based on Salp Swarm Algorithm to Enhance Efficiency of MRI Images

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

Automated segmentation and classification technique for brain stroke

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