Brain tumor detection using optimisation classification based on rough set theory

Rajesh, T.; Malar, R. Suja Mani; Geetha, M.

doi:10.1007/s10586-018-2111-5

Cited by 31 publications

(15 citation statements)

References 9 publications

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“…However, the classification model achieves only 88% of accuracy and lacks time complexity. An innovative system for detecting and classifying brain tumors is described in [32]. In this system, the rougPrh set theory is developed for the process of feature extraction, and the Particle Swarm Optimization Neural Network (PSO-NN) is utilized for classifying the abnormalities in the MRI brain images.…”

Section: Related Workmentioning

confidence: 99%

Pattern Descriptors Orientation and MAP Firefly Algorithm Based Brain Pathology Classification Using Hybridized Machine Learning Algorithm

Deepa¹,

Murugappan

Sumithra

et al. 2022

IEEE Access

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Magnetic Resonance Imaging (MRI) is a significant technique used to diagnose brain abnormalities at early stages. This paper proposes a novel method to classify brain abnormalities (tumor and stroke) in MRI images using a hybridized machine learning algorithm. The proposed methodology includes feature extraction (texture, intensity, and shape), feature selection, and classification. The texture features are extracted by intending a neoteric directional-based quantized extrema pattern. The intensity features are extracted by proposing the clustering-based wavelet transform. The shape-based extraction is preformed using conventional shape descriptors. Maximum A Priori (MAP) based firefly algorithm is proposed for feature selection. Finally, hybridized support vector-based random forest classifier is used for the classification. The MRI brain tumor and stroke images are detected and categorized into four classes which are a high-grade tumor, a low-grade tumor, an acute stroke, and a sub-acute stroke. Besides, three different regions are identified in tumor detection such as edema, and tumor (necrotic and non-enhancing) region. The accuracy of the proposed method is analyzed using various performance metrics in comparison with the few state-of-the-art classification methods. The proposed methodology successfully achieves a reliable accuracy of 88.3% for classifying brain tumor cases and 99.2% for brain stroke classification. The best F-score of 0.91 and the least FPR of 0.06 are attained while considering brain tumor classification against the proposed HSVFC. Likewise, HSVFC has 0.99 as the best F-score and a 0.0 FPR in the case of brain stroke classification. The experimental analysis offers a maximum mean accuracy of different classifiers for categorizing MRI brain tumor are 76.

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Section: Related Workmentioning

confidence: 99%

Pattern Descriptors Orientation and MAP Firefly Algorithm Based Brain Pathology Classification Using Hybridized Machine Learning Algorithm

Deepa¹,

Murugappan

Sumithra

et al. 2022

IEEE Access

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“…Various methods can be used for imaging a medical object, such as angiogram, brain scan, computerized tomography (CT)-scan, diffusion tensor imaging, functional magnetic resonance imaging (fMRI) [2], MRI, magnetic resonance spectroscopy (MRS), positron emission tomography (PET), and Biopsy [3]. Some literature states that MRI is the best examination tool for its relatively safe radiation hazards [4] and the high accuracy rate. In an MRI examination, the patient is placed on a bed and inserted into a magnetic tube.…”

Section: Introductionmentioning

confidence: 99%

Brain tumor segmentation using double density dual tree complex wavelet transform combined with convolutional neural network and genetic algorithm

Samosir¹,

Abdurachman

Gaol

et al. 2022

IJ-AI

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<span>Image segmentation is often faced by low contrast, bad boundaries, and inhomogeneity that made it difficult to separate normal and abnormal tissue. Therefore, it takes long periodto read and diagnose brain tumor patients. The aim of this study was to applied hybrid methods to optimize segmentation process of magnetic resonance image of brain. In this study, we divide the brain tumor images with double density dual-tree complex wavelet transform (DDDTCWT), continued by convolutional neural network (CNN), and optimized by genetic algorithm (GA) with 48 combinations yielding excellent results. The F-1 score was 99.42%, with 913 images test data. The training images consist of 1397 normal MRI images and 302 tumor magnetic resonance imaging (MRI) images resized by 32 x32 pixels. The DDDTCWT transforms the input images into more detail than ordinary wavelet transforms, and the CNNs will recognize the pattern of the output images. Additionally, we applied the GA to optimize the weights and biases from the first layer of the CNNs layers. The parameters used for evaluating were dice similarity coefficient (DSC), positive present value (PPV), sensitivity, and accuracy. The result showed that the combination of DDDTCWT, CNN, and GA could be used to brain MRI images and it generated parameters value more that 95%.</span>

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“…In the second stage, segmentation is done using the level-set methodology. Nayak et al 17 Rajesh et al 18 have employed the rough set theory for feature extraction and swam optimization neural network to classify MR images into normal and abnormal. Ozyurt et al 19 have used a machine learning algorithm and fuzzy C-means (FCM) with super-resolution for the segmentation and classification of brain tumors.…”

Section: Introductionmentioning

confidence: 99%

Classification of brain tumor from magnetic resonance images using probabilistic features and possibilistic Hanman–Shannon transform classifier

Asthana

Hanmandlu²,

Vashisth

2021

Int J Imaging Syst Tech

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A brain tumor is considered one of the deadliest forms among all types of cancer due to its aggressive nature leading to patients' low survival rate. Detection and classification of brain tumors have a significant impact on treatment planning and patient survival. The significance and importance of this work lie in the formulation of several probabilistic features that represent higher-level probabilistic uncertainty. To create these features, the gain function of probabilistic Hanman transform is replaced with the gain functions of Shannon, Renyi, and Tsallis entropy functions thus paving a way to the corresponding hybrid transforms, Hanman-Shannon, Hanman-Renyi, and Hanman-Tsallis transforms. The new features are extracted from brain MR images to detect and classify tumor by developing the possibilistic Hanman-Shannon transform classifier. This uses the t-normed errors between the training and testing features. The proposed system when evaluated on the two Brain MRI datasets yields the highest accuracy of around 99%.

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Brain tumor detection using optimisation classification based on rough set theory

Cited by 31 publications

References 9 publications

Pattern Descriptors Orientation and MAP Firefly Algorithm Based Brain Pathology Classification Using Hybridized Machine Learning Algorithm

Pattern Descriptors Orientation and MAP Firefly Algorithm Based Brain Pathology Classification Using Hybridized Machine Learning Algorithm

Brain tumor segmentation using double density dual tree complex wavelet transform combined with convolutional neural network and genetic algorithm

Classification of brain tumor from magnetic resonance images using probabilistic features and possibilistic Hanman–Shannon transform classifier

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