Brain Tumor Detection Using MRI Image Analysis

Bhattacharyya, Debnath; Kim, Tai-hoon

doi:10.1007/978-3-642-20998-7_38

Cited by 49 publications

(21 citation statements)

References 5 publications

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“…Handling of different variations of cell appearance and split contacting cells is the superior challenge of brain tumor detection. Sparse reconstruction is utilized by an automatic cell detection framework, to discover split contacting cells and to handle different variations in cell appearance an adaptive dictionary learning method is utilized . To improve the nature of the picture for the further image processing, computerized pictures are harmonized.…”

Section: Related Work: a Brief Reviewmentioning

confidence: 99%

Early detection of brain cancer using association allotment hierarchical clustering

Tamilmani

Sivakumari

2019

Int J Imaging Syst Tech

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Medical applications uses the computer assisted diagnosis system for recognizing the enormous amount of diseases, but cancer is one of the major challenge to the doctors because it is difficult to recognize in earlier stage with accurate manner. To overcome these issues, in this article, an early detection of brain cancer using Association Allotment Hierarchical Clustering technique is proposed. The proposed automatic detection process includes various processing steps such as preprocessing, image segmentation, feature extraction, selection, and cancer identification process. Initially the microscopic images are captured which contaminated several noises that have been removed by applying the mutual piece‐wise linear transformation filtering approach. The method successfully eliminates irrelevant information and enhances the quality of the captured biopsy image. After that affected cancer cell region has been segmented with the help of Association Allotment Hierarchical Clustering method which examines the cell, tissues, and relevant border while segmenting the cancer cell. From the segmented region, different textures, statistical features are extracted depending on the tissue level, cell level, region, and contour level. Then to improve the performance of classification, the optimum features are selected using gray wolf optimization. The novelty of the proposed method is to give a better performance and the accuracy is obtained nearly 100%. Finally, the selected features are classified using neural network. Experimental result demonstrates that the performance of proposed method in terms of segmentation and computation time is better when compared with state‐of‐art approaches.

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Section: Related Work: a Brief Reviewmentioning

confidence: 99%

Early detection of brain cancer using association allotment hierarchical clustering

Tamilmani

Sivakumari

2019

Int J Imaging Syst Tech

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“…Brain tumor is a mass of tissue that grows out of control of the normal forces that regulates growth [2]. The Brain tumor is the most common, occurring malignancy among human beings [3]. The brain abnormality detection and segmentation of MRI images is an exceptionally hard to specify and crucial to assess which is utilized as a part of surgical and restorative arranging and appraisal.…”

Section: Related Workmentioning

confidence: 99%

Investigation the Effect of Using Gray Level and RGB Channels on Brain Tumor Image

Salamh¹

2017

Computer Science &Amp; Information Technology (CS &Amp; IT)

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“…The literature presents a large number of algorithms for segmenting brain tumors. The techniques such as thresholding and morphological techniques 8 are employed. Additionally, watershed method, 9 asymmetry analysis, 10 region growing approach, 11 atlas-based method, 12 interactive algorithm, 13 contour/surface evolution method, 14,15 and learning methods such as supervised 16 and unsupervised 17 are employed.…”

Section: Introductionmentioning

confidence: 99%

Multiclassifier for severity‐level categorization of glioma tumors using multimodal magnetic resonance imaging brain images

Mahesh

Renjit²

2019

Int J Imaging Syst Tech

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Brain tumor segmentation and classification is a crucial challenge in diagnosing, planning, and treating brain tumors. This article proposes an automatic method that categorizes the severity level of the tumors to render an effective diagnosis. The proposed fractional Jaya optimizer‐deep convolutional neural network undergoes the severity classification based on the features obtained from the segments of the magnetic resonance imaging (MRI) images. The segments are obtained using the particle swarm optimization that ensures the optimal selection of the segments from the MRI image and yields the core tumor and the edema tumor regions. The experimentation using the BRATS database reveals that the proposed method acquired a maximal accuracy, specificity, and sensitivity of 0.9414, 0.9429, and 0.9708, respectively.

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Brain Tumor Detection Using MRI Image Analysis

Cited by 49 publications

References 5 publications

Early detection of brain cancer using association allotment hierarchical clustering

Early detection of brain cancer using association allotment hierarchical clustering

Investigation the Effect of Using Gray Level and RGB Channels on Brain Tumor Image

Multiclassifier for severity‐level categorization of glioma tumors using multimodal magnetic resonance imaging brain images

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