Comparative study on brain tumor detection techniques

Haritha, D.

doi:10.1109/scopes.2016.7955668

Cited by 14 publications

(12 citation statements)

References 18 publications

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“…There are numerous studies in brain imaging domain such as brain abnormalities detection, brain mapping, brain stroke analysis, brain tumor volume analysis, brain tissue classification, epilepsy analysis , and brain tumor segmentation in a near future [1][2][3][4][5][6][7][8]. Due to the advancement and innovation of digital media technologies, these brain imaging applications are beneficial to the patients that experience with the brain abnormalities problem.…”

Section: Introductionmentioning

confidence: 99%

Automated segmentation and detection of T1-weighted magnetic resonance imaging brain images of glioma brain tumor

et al. 2020

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There are numerous studies on brain imaging applications. The statistics in Malaysia showed that glioma is one of the most common type disease in brain tumor. Glioma brain tumor is an abnormal growth of glial cells inside the brain tissues which known as cerebral tissues. Radiologist commonly used Magnetic Resonance Imaging (MRI) image sequences to diagnose the brain tumor. However, manual examination of the brain tumor diagnosis by radiologist is difficult and time-consuming task as tumors are occurred in variability of shape and appearance. They will also inject a gadolinium contrast agent to enhance the image modality which will give the side effects to the patients. Therefore, this paper presents an automated segmentation and detection of MRI brain images using Sobel edge detection and mathematical morphology operations. The total of 30 glioma T1-Weighted MRI brain images are obtained from Brain Tumor Image Segmentation Benchmark (BRATS). The results of segmentation and detection are quantitatively evaluated by using Area Overlap which produced the accuracy rate of 80.2% and shows that the presented methods are promising.

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Section: Introductionmentioning

confidence: 99%

Automated segmentation and detection of T1-weighted magnetic resonance imaging brain images of glioma brain tumor

et al. 2020

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“…Brain tumour detection is significant algorithm for identifying the tumour in brain [1]. Brain tumour is an abnormal cell growth in the human brain; it is classified into two types such as benign and malignant.…”

Section: Introductionmentioning

confidence: 99%

Image Analysis for MRI Based Brain Tumour Detection Using Hybrid Segmentation and Deep Learning Classification Technique

Rao¹,

Lingappa²

2019

IJIES

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Indefinite and uncontrollable growth of cells leads to tumours in the human brain. A proper treatment and early diagnosis of brain tumours are essential to prevent permanent damage to the brain. In medical image diagnosis, the tumour segmentation and classification schemes are used for identifying the tumour and non-tumour cells in the brain. The automatic classification is a challenging task which utilizes the traditional methods due to its more execution time and ineffective decision making. To overcome this problem, this research proposes an automatic tumour classification method named as Hybrid Kernel based Fuzzy C-Means clustering-Convolutional Neural Network (Hybrid KFCM-CNN) method. The performance of Hybrid KFCM-CNN method is validated using T1-Weighted Contrast Enhanced Magnitude Resonance Imaging (T1-W CEMRI) database. A tumour portion is segmented from the MRI brain image by using Hybrid KFCM method. After the segmentation process, statistical features and super pixels based SIFT, Discrete Cosine Transform (DCT) methods were performed on the segmented image to enhance feature subsets. The best feature values were given to the CNN classifier as an input; it is classified into different types of brain tumours such as Meningioma, Glioma and Pituitary. The experimental outcome shows that the Hybrid KFCM-CNN method improved the accuracy of brain tumour classification up to 14.06 % than existing classifiers: SVM and CNN.

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“…The least hostile kind of brain tumor by the large named as a benign brain tumor, they initiate from the cells inside or encompassing the brain that does not envelopes any malignant growth cells, develop gradually and normally has been the clear fringes. 1 Harmful brain tumor consolidates cancer cells and they do not have clear fringes. Metastatic brain tumor starts as malignancy elsewhere in the body and spreads to the brain.…”

Section: Introductionmentioning

confidence: 99%

“…Essential brain tumors might be grouped into benign and malignant tumors. The least hostile kind of brain tumor by the large named as a benign brain tumor, they initiate from the cells inside or encompassing the brain that does not envelopes any malignant growth cells, develop gradually and normally has been the clear fringes . Harmful brain tumor consolidates cancer cells and they do not have clear fringes.…”

Section: Introductionmentioning

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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Comparative study on brain tumor detection techniques

Cited by 14 publications

References 18 publications

Automated segmentation and detection of T1-weighted magnetic resonance imaging brain images of glioma brain tumor

Automated segmentation and detection of T1-weighted magnetic resonance imaging brain images of glioma brain tumor

Image Analysis for MRI Based Brain Tumour Detection Using Hybrid Segmentation and Deep Learning Classification Technique

Early detection of brain cancer using association allotment hierarchical clustering

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