Malignant Brain Tumor Detection

Singh, Amarjot; Bajpai, Shivesh; Karanam, Srikrishna; Choubey, Akash; Raviteja, Thaluru

doi:10.7763/ijcte.2012.v4.626

Cited by 16 publications

(13 citation statements)

References 14 publications

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“…The National Brain Tumor Foundation (NBTF) for research in the United States estimated the death of 13000 patients while 29000 underwent primary brain tumor diagnosis every year [1]. Depending on the origin and growth, brain tumor is classified into two types: The primary brain tumor develops at the original site of the tumor, and the secondary brain tumor is the cancer that spreads to the other parts of the body.…”

Section: Introductionmentioning

confidence: 99%

Survey of Brain Tumor Segmentation Techniques on Magnetic Resonance Imaging

Hameurlaine¹,

Moussaoui²

2019

Nano BioMed ENG

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Brain tumor extraction is challenging task because brain image and its structure are complicated that can be analyzed only by expert physicians or radiologist. Brain tumor detection and segmentation is one of the most challenging and time consuming task in medical image processing. The image segmentation is a very difficult job in the image processing and challenging task for clinical diagnostic tools. MRI (Magnetic Resonance Imaging) is a visualization medical technique, which provides plentiful information about the human soft tissue, which helps in the diagnosis of brain tumor. Accurate segmentation of the MRI images is extremely important and essential for the exact diagnosis by computer aided clinical tools. There are different types of segmentation algorithms for MRI brain images. This paper is to check existing approaches of Brain tumor segmentation techniques in MRI image for Computer aided diagnosis.

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Section: Introductionmentioning

confidence: 99%

Survey of Brain Tumor Segmentation Techniques on Magnetic Resonance Imaging

Hameurlaine¹,

Moussaoui²

2019

Nano BioMed ENG

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“…Brain tumor is a growing abnormal cell in the brain or central spin canal. In USA ( [1]), National Brain Tumor Society estimates that every year 13.000 patients die, and 29.000 patients suffer from primary brain tumors (i.e. primary tumor is a tumor starts in the brain).…”

Section: Introductionmentioning

confidence: 99%

Fully Automatic Brain Tumor Segmentation using End-To-End Incremental Deep Neural Networks in MRI images

Naceur

Saouli

Akil

et al. 2018

Computer Methods and Programs in Biomedicine

193

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Background and Objective: Nowadays, getting an efficient Brain Tumor Segmentation in Multi-Sequence MR images as soon as possible, gives an early clinical diagnosis, treatment and follow-up. The aim of this study is to develop a new deep learning model for the segmentation of brain tumors. The proposed models are used to segment the brain tumors of Glioblastomas (with both high and low grade). Glioblastomas have four properties: different sizes, shapes, contrasts, in addition, Glioblastomas appear anywhere in the brain.Methods: In this paper, we propose three end-to-end Incremental Deep Convolutional Neural Networks models for fully automatic Brain Tumor Segmentation. Our proposed models are different from the other CNNs-based models that follow the technique of trial and error process which does not use any guided approach to get the suitable hyper-parameters. Moreover, we adopt the technique of Ensemble Learning to design a more efficient model. For solving the problem of training CNNs model, we propose a new training strategy which takes into account the most influencing hyper-parameters by bounding and setting a roof to these hyper-parameters to accelerate the training.Results: Our experiment results reported on BRATS-2017 dataset. The proposed deep learning models achieve the state-of-the-art performance without any post-processing operations. Indeed, our models achieve in average 0.88 Dice score over the complete region. Moreover, the efficient design with the advantage of GPU implementation, allows our three deep learning models to achieve brain segmentation results in average 20.87 seconds.Conclusions: The proposed deep learning models are effective for the segmentation of brain tumors and allow to obtain high accurate results. Moreover, the proposed models could help the physician experts to reduce the time of diagnostic.

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“…these features make CT and MRI more suitable for the detection of tumor . [12] Wavelet transforms is a new area of technology. utilizes wavelet analysis based image fusion to enhance the efficiency of brain tumor detection.…”

Section: Introductionmentioning

confidence: 99%

Image Fusion Based Brain Tumor Detection

Kumar¹

2019

IJRASET

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It is very important to detect brain tumor at right time in brain tumor diagnosis. We are using image fusion for CT and MRI images. Image fusion is use to get more information for brain tumor diagnosis. Resultant fused image of CT and MRI images will improve accuracy of tumor detection. MRI and CT scan images are very useful in tumor disease diagnosis. Brain image is easily degraded by noises. For noise minimizing and enhancing the image quality we use discrete wavelet transform method. It provides enhances image quality. Here we are using image fusion method and morphological image processing for increasing image intensity. Image segmentation used to detect tumor portion accurately and indicate about growing area of tumor.

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Malignant Brain Tumor Detection

Cited by 16 publications

References 14 publications

Survey of Brain Tumor Segmentation Techniques on Magnetic Resonance Imaging

Survey of Brain Tumor Segmentation Techniques on Magnetic Resonance Imaging

Fully Automatic Brain Tumor Segmentation using End-To-End Incremental Deep Neural Networks in MRI images

Image Fusion Based Brain Tumor Detection

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