Brain Tumor Detection Using Anisotropic Filtering, SVM Classifier and Morphological Operation from MR Images

Rashid, Mosfeq; Mamun, Md. Al; Hossain, Md. Ali; Uddin, Md. Palash

doi:10.1109/ic4me2.2018.8465613

Cited by 44 publications

(15 citation statements)

References 4 publications

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“…Cardiac ultrasound images [2] are used to assess cardiac physiological indicators, coronary heart diseases, diagnosing heart failure covering a wide range of clinical applications. Detection of brain tumor position [16] using MRI and removal of defective pixels from the image is quite challenging. The identification of potential tumors on computer tomography images for the early stage oral cavity cancer detection [17] requires suitable filtering algorithms.…”

Section: Methods Featuresmentioning

confidence: 99%

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A Review on Performance Analysis of PDE based Anisotropic Diffusion Approaches for Image Enhancement

Thakur¹,

Khan

Sharma³

2021

IJCAI

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Partial differential equation based anisotropic diffusion techniques are used extensively in computer vision for image enhancement and de-noising. Anisotropic diffusion is found to be an efficient and low computational complexity approach that has overcome the undesirable effects of linear smoothing filters and now is popular in prominent research areas of enhancing the quality of low contrast images and speckle noise reduction from geological, industrial, and medical images. This paper presents state-of-theart anisotropic diffusion technique and a comprehensive survey on various advancements in anisotropic diffusion for image enhancement and de-noising. The capability of anisotropic diffusion for enhancing the quality of low contrast images and speckle noise reduction from medical and industrial images are further explored. Various quality measures used to validate the performance are studied. The major research issues and possible future scopes in anisotropic diffusion filtering are also discussed. Povzetek: Prispevek predstavi pregled in novo metodo na področju anizotropnedifuzije za povečevanje slik in zmanjševanje šuma.

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Section: Methods Featuresmentioning

confidence: 99%

“…)(∇ 2 𝐼/𝐼) 2 (15) and 𝑞 0 (𝑡) is speckle scale function. The edge preservation sensitivity of this method was further examined [15] and presented as detail preserving anisotropic diffusion where the orientation of edges were made to stabilize while removing speckle noise as indicated in Eq (16).…”

Section: Speckle Noise Reductionmentioning

confidence: 99%

A Review on Performance Analysis of PDE based Anisotropic Diffusion Approaches for Image Enhancement

Thakur¹,

Khan

Sharma³

2021

IJCAI

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“…M. Rashid et al [8] examined the MRI image and a method for an even clearer vision of the position acquired by the tumor. MRI brain image is an input of the system.…”

Section: Literature Surveymentioning

confidence: 99%

A Framework for Brain Tumor Segmentation and Classification using Deep Learning Algorithm

Kulkarni¹,

Sundari²

2020

IJACSA

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The brain tumor is a cluster of the abnormal tissues, and it is essential to categorize brain tumors for treatment using Magnetic Resonance Imaging (MRI). The segmentation of tumors from brain MRI is understood to be complicated and also crucial tasks. It can be further use in surgery, medical preparation, and assessments. In addition to this, the brain MRI classification is also essential. The enhancement of machine learning and technology will aid radiologists in diagnosing tumors without taking invasive steps. In this paper, the method to detect a brain tumor and classification has been present. Brain tumor detection processes through pre-processing, skull stripping, and tumor segmentation. It is employing a thresholding method followed by morphological operations. The number of training image influences the feature extracted by the CNN, then CNN models overfit after some epoch. Hence, deep learning CNN with transfer learning techniques has evolved. The tumorous brain MRI is classified using CNN based AlexNet architecture. Further, the malignant brain tumor is classified using GooLeNet transfer learning architecture. The performance of this approach is evaluated by precision, recall, F-measure, and accuracy metrics.

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“…The accuracy of those MR images was enhanced by applying it on quality image processing techniques. Commonly known conventional machine learning-based mechanism used for detecting brain tumor are random forest (RF), k-nearest neighbor's algorithm (KNN) and support vector machines (SVM) [5][6][7][8] As we say among the image processing, the processing on brain images are most complex task then others. The medical diagnosis can be made simple by using medical image processing by viewing the internal structures of unseen human organs.…”

Section: Introductionmentioning

confidence: 99%

Advanced Technique for Classification and Detection of Brain Tumor in Magnetic Resonance Images

Sathishkannan¹,

B²,

Rupashini³

et al. 2021

JUSST

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In the medical world, most challenging disease is Brain tumor. Brain tumors formed inside the brain as an abnormal cell. It is a mass of tissues which results in hormonal changes results in mortality. In the recent years, various brain tumor detection techniques are evolved. We propose, a novel brain tumor detection technique is proposed to detect tumors accurately in given brain MR image and also it classifies the given brain MR image is normal or abnormal. At first the preprocessing is performed by median filtering and segmentation by means of morphological technique. Then the Gray Level Co-occurrence Matrix (GLCM) is applied to extract the texture features. Then, the derived features are applied to classification using three classifiers such as Naïve Bayes, Multilayer perceptron, and Decision Tree C4.5 classifiers. By conducting experiments, the proposed technique is assessed and validated for performance as well as quality analysis based on accuracy, sensitivity and specificity on brain MR images. In experimental section, the performance of all three classifiers are compared in which the decision tree C4.5 algorithm provides better performance with 75% of accuracy, 79% of sensitivity and 56% of specificity.

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Brain Tumor Detection Using Anisotropic Filtering, SVM Classifier and Morphological Operation from MR Images

Cited by 44 publications

References 4 publications

A Review on Performance Analysis of PDE based Anisotropic Diffusion Approaches for Image Enhancement

A Review on Performance Analysis of PDE based Anisotropic Diffusion Approaches for Image Enhancement

A Framework for Brain Tumor Segmentation and Classification using Deep Learning Algorithm

Advanced Technique for Classification and Detection of Brain Tumor in Magnetic Resonance Images

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