Automatic Brain Tumor Detection and Classification of Grades of Astrocytoma

Devi, N. Bharatha; Bhattacharyya, Kaustubh

doi:10.1007/978-981-10-6890-4_11

Cited by 8 publications

(3 citation statements)

References 2 publications

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“…Hawaii et al developed an automated method for brain tumor detection based on the deep neural networks (DNN) [4]. The created deep neural network technique simultaneously uses both global and local contextual data [4]. The problems with the tumor label mismatch are removed using a two-phase training technique.…”

Section: Damodharan and Raghavanmentioning

confidence: 99%

“…In particular, knowledge-based techniques, Fuzzy Clustering means, K-means, artificial neural networks, support vector machines, an expectationmaximization (EM) algorithm techniques are the most common methodologies used in the regionbased segmentation to obtain the necessary information data from all types of medical imaging [3]. Several of these methods have been used to detect tumors in MRI data [4].…”

Section: Introductionmentioning

confidence: 99%

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Deep Neural Network Approach Based Segmentation, Detection and Classification of Brain Tumor

Zaman

Din

et al. 2022

JERR

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The segmentation, detection and extraction of malignant tumor regions from magnetic resonance (MR) images are challenging tasks in medical image analysis. Approaches based on machine and deep learning have been introduced, which performed better than traditional image processing methods. However, many approaches still show limited ability due to the complex dataset and image modalities. This study evaluated the deep learning approach's performance and traditional image processing algorithms for medical imaging segmentation, detection, and classification. The proposed system comprises multiple stages. The Median filters are used in the pre-processing step, and morphological operation and Otsu thresholding are used to segment MR images. Discrete Wavelet Transform (DWT) algorithm is considered in the extraction features, and their classification is executed by a convolutional neural network (CNN) and support vector machine (SVM) algorithms. The Mat lab has been used for simulation and experimental findings to evaluate the suggested method's performance on the brain's complex and highly 2D structures. The results show that the methodology is reliable and efficient, with 93.5% accuracy.

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Section: Damodharan and Raghavanmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Deep Neural Network Approach Based Segmentation, Detection and Classification of Brain Tumor

Zaman

Din

et al. 2022

JERR

View full text Add to dashboard Cite

show abstract

“…Cerebrum meta state usually called as brain Mets are much more normal. Brain tumor is the one of the leading causes of death in the world [1]. Medical anatomical imaging for example X-ray beams and later followed by computer tomography (CT) and MRI each one of these have been extensively utilized by scientist in the medical image processing extraction of useful organic information from X-ray brain image is not very effective due to their limitations of poor image quality and not strong interaction with the light element CT imaging is very useful for viewing changes in bony structure i.e.…”

Section: Introductionmentioning

confidence: 99%

Review on different Brain Tumor Analysis Techniques

Mudukshiwale¹

2019

IJRASET

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A literature review is described on cerebrum (brain) tumor diagnosis. The aim of this survey is to provide an outline for those who are new to the field of image processing, and also to provide a reference for those searching for literature in these applications. Tumor is because of an abnormal development of cells (tissues) inside the brain. Magnetic Resonance Imaging (MRI), Computer Tomography (CT) imaging techniques are used for early detection of abnormal changes in tumor tissues or cells. Its correct detection and identification at an early stage is the only way to get cure. Brain tumor tissues may become malignant (cancerous) if not diagnosed at right time. A recent couple of years various image processing algorithms have been proposed for correct and efficient computer aided diagnosis of cerebrum tumors. An algorithm effectively works on CT, MRI images. It is been observed that an automatic segmentation method using Convolution Neural Network (CNN) with 3*3 kernels provide deeper architecture and positive results against over fitting. Watershed segmentation algorithm removes the salt & pepper noise without disturbing edges. It is very easy for automatic and accurate calculation of tumor area. Sobel edge detection based improved edge detection algorithm provide superior performance over conventional segmentation algorithm K-Mean algorithm used

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Fractional probabilistic fuzzy clustering and optimization based brain tumor segmentation and classification

Jemimma

Vetharaj

2022

Multimed Tools Appl

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Automatic Brain Tumor Detection and Classification of Grades of Astrocytoma

Cited by 8 publications

References 2 publications

Deep Neural Network Approach Based Segmentation, Detection and Classification of Brain Tumor

Deep Neural Network Approach Based Segmentation, Detection and Classification of Brain Tumor

Review on different Brain Tumor Analysis Techniques

Fractional probabilistic fuzzy clustering and optimization based brain tumor segmentation and classification

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