A hybrid approach based segmentation technique for brain tumor in MRI Images

Anithadevi, D.; Perumal, K.

doi:10.48550/arxiv.1603.02447

Cited by 2 publications

(3 citation statements)

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“…The key goal is to get the positions of suspected regions to support diagnostic radiologists. Sathya and Kayalvizhi [8] designed a multilevel thresholding that depends on Adaptive Bacterial Foraging (ABF) algorithm for MRI brain image segmentation. George and Karnan [9] designed a brain tumor segmentation using Adaptive threshold method.…”

Section: Image Segmentationmentioning

confidence: 99%

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Survey of brain tumor segmentation with deep neural networks

Kumar¹,

Sarath²

2022

ijhs

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A brain tumour is a serious condition that, if not diagnosed and treated early on, can lead to death. Researchers have proposed a variety of traditional and recently developed deep learning based segmentation and classification approaches for determining the condition of the tumor. Deep learning is found to be efficient and robust for classification and segmentation as it detects the fine-to-coarse information about the tumors. The main component of deep learning is layered neural network architecture popularly known as convolutional neural network. Distinct information from brain images can be acquired and analysed depending on the architecture. In order to achieve high segmentation and classification accuracy, more research is required in this area. In this paper presents a review of state-of-the-art deep learning methods for brain tumor segmentation and deep learning neural networks, clearly highlighting their building blocks and different strategies. Finally, this article implying about present status on segmentation and classification of tumor-based image processing through deep learning models.

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Section: Image Segmentationmentioning

confidence: 99%

“…They happen in 10-15 percent of malignant growth patients. Primary brain tumours are not normally metastasised to other areas of the body [8]. The number of MRI images to be interpreted in manual diagnosis is large enough to make visual interpretation based readings costly, unreliable and complex.…”

Section: Introductionmentioning

confidence: 99%

Survey of brain tumor segmentation with deep neural networks

Kumar¹,

Sarath²

2022

ijhs

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“…This facilitates a better understanding of tumor characteristics and development trends for doctors, enabling them to develop personalized treatment plans for patients [10]. Anithadevi et al [11] proposed an image segmentation method that combines region growing and thresholding. This hybrid approach uses a center pixel/fixed seed point for region growing segmentation and a single threshold for thresholding segmentation, which helps improve the results of region growing segmentation.…”

Section: Introductionmentioning

confidence: 99%

Review of deep learning-driven MRI brain tumor detection and segmentation methods

Zhang,

Luo,

Chen

et al. 2023

ACSS

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The application of deep learning in the field of medical imaging has become increasingly widespread, greatly promoting the advancement and development of Magnetic Resonance Imaging (MRI) brain tumor detection and segmentation techniques. Therefore, a comprehensive review of deep learning-based methods for MRI brain tumor detection and segmentation was conducted. This review introduces the basic concepts of brain tumors and MRI brain tumor detection and segmentation, discusses the specific applications and typical methods of deep learning in MRI brain tumor detection and segmentation, and analyzes and compares the performance and advantages and disadvantages of different methods. Additionally, representative brain tu-mor segmentation dataset (BraTS) and its evaluation metrics are introduced, upon which the performance of various deep learning-based brain tumor segmentation methods on the BraTS 2019-2022 dataset is compared. Lastly, the challenges and future development trends in deep learning-based MRI brain tumor detection and segmentation methods are summarized and anticipated.

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A hybrid approach based segmentation technique for brain tumor in MRI Images

Cited by 2 publications

References 5 publications

Survey of brain tumor segmentation with deep neural networks

Survey of brain tumor segmentation with deep neural networks

Review of deep learning-driven MRI brain tumor detection and segmentation methods

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