Detection of Brain Tumor in MRI Images by Applying Segmentation and Area Calculation Method Using SCILAB

Kurnar, Mahesh; Sinha, Aman; Bansode, Nutan V.

doi:10.1109/iccubea.2018.8697713

Cited by 20 publications

(5 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…T. M. Shahriar Sazzad, et.al (2019) suggested ancomputer assisted scheme in which the brain tumor was detected by integrating MRI gray-scale images [21]. An automated approach was suggested in this study that comprised enhancement at the preliminary phase for minimizing the gray-scale color variations.…”

Section: Literature Reviewmentioning

confidence: 96%

Comparative Study of Convolution Neural Network with GLCM Approach and Superpixel based Spectral Clustering for Brain Tumor Detection

Kaur¹,

Sharma²

2022

Preprint

View full text Add to dashboard Cite

Extensive growth in the volume of irregular brain cells is known as brain tumor. Human brain is surrounded by stiff skull. There are various issues that occur due to the growth of any tumor inside this restricted space. The malignant and benign are two main categories of the brain tumor. The skull is pressurized to enlarge from inside in case of growth of any benign or malignant tumor. This tumor leads to harm in brain and it may be dangerous to life also. The brain tumor is divided into two kinds - primary or secondary. The brain tumor detection techniques have various phases. In this paper, comparative study of CNN with GLCM approach and superpixel based spectral clustering is done tumor. This work takes into account metrics like accuracy, sensitivity and specificity for drawing the comparison between both the techniques.

show abstract

Section: Literature Reviewmentioning

confidence: 96%

Comparative Study of Convolution Neural Network with GLCM Approach and Superpixel based Spectral Clustering for Brain Tumor Detection

Kaur¹,

Sharma²

2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Segmentation of brain tumors plays an important role in detecting tumor cells. In the literature, there are many automated detection and classification algorithms like edge detection, contour/Atlas detection, ML, and deep learning techniques 15,17 – 28 are available. Although supervised learning methods achieve better performance, they rely on manual feature extraction and selection approaches.…”

Section: Related Workmentioning

confidence: 99%

MAEU‐NET: A novel supervised architecture for brain tumor segmentation

Kumar,

Biswal

2023

Int J Imaging Syst Tech

View full text Add to dashboard Cite

A brain tumor is an abnormal growth of cells that damages the neural system and may lead to severe conditions. These cells are irregular in shape and size, and as a result, the manual analysis of these cells from magnetic resonance (MR) images is a cumbersome and time‐consuming task. To alleviate these issues, a novel U‐Net inspired MAEU‐Net (Multiscale context Aggregation and Edge activation U‐Net) is implemented to detect the brain tumor pixels from MR images more precisely. The encoder block of the proposed model is improved by integrating with multiscale feature aggregation and convolution block attention modules (CBAM) enabling the model to extract both pertinent low‐level and high‐level features. Further, the extracted features are subjected to a parallel pooling module to elevate the features that extract the tumor pixels. In addition to this, the output features of the last encoder are fed to a parallel dilated context aggregation module to mitigate the hyper‐parameter size and further elevate the receptive field strength of the proposed model. Apart from this, edge‐enhanced modules are incorporated with skip connections which concatenate the feature maps of the decoder with respective edge‐enhanced encoder feature maps. As a result, this module increases the aspect ratio and reduces blurry edge problems. To illustrate the performance of the proposed MAEU‐Net, it is trained and tested using Kaggle LGG, BraTS 2018, and BraTS 2021 publicly available datasets. The proposed model outperformed the previously existing models by achieving the test loss, dice coefficient, sensitivity, and specificity of 0.0590, 0.901, 0.942, and 0.998, respectively. Simultaneously, the proposed model is quantitatively analyzed by performing ablation and comparative studies and presented that the proposed MAEU‐Net is superior to other previous existing models like U‐Net++, SegNet, etc.

show abstract

“…They implemented an approach through free surfer system software to search the width of cortical and the compute variance among benign and malignant. Kilic, H., Yuzgec, U and Karakuzu, C et al, 2018 [18] implemented a novel technique for the development of ant lion optimization (ALO) that encouraged by hunting method of ant lion. The main issue of the used method is maximum time consuming, so proposed a new randomized walk technique to overcome this issue.…”

Section: Hasan S K and Ahmad M Et Al 2018 [17]mentioning

confidence: 99%

Enhancement in Brain Image Segmentation using Swarm Ant Lion Algorithm

Bansal¹,

Kaur²,

Kaur³

2019

IJITEE

View full text Add to dashboard Cite

Brain tumor image segmentation is a play a vital role in the medical field or medical processing. Patient treatment with brain tumors is the significant level determine on early-stage detection of these tumors. Early stage detection of Brain Tumors will enhance the patient lives. The disease of brain tumors by a neurologist frequently uses a manual image segmentation that is a hard and time-consuming process, because of necessary automatic image segmentation. Nowadays, automatic image segmentation is very popular and can solve the issue of tumor brain image segmentation with better performance. The main motive of this research work is to provide a survey of MRI image based brain tumor segmentation techniques. There are various existing study papers, focusing on new techniques for Reasonable Magnetic Image-based brain tumor image segmentation. The main problem is considered a complicated process, because of the variability of tumor area of the complexity of determining the tumor position, size, shape and texture. In this research work, mainly worked on interference method, feature extraction, morphological operators, edge detection methods of gray level and Swarm Ant Lion Optimization based on brain tumor shape growing segmentation to optimize the image complexity and enhance the performance. In new algorithm implemented an inspiring nature method for segmentation of brain tumor image using hybridization of PSOA and ALO is also called a Swarm Ant Lion method. Evaluate the performance metrics with image quality factor (PSNR), Error Rate (MSE), and Exact value (Accuracy Rate). In research work, improve the performance metrics with PSNR and Accuracy Rate and reduce the error rates and compared with the existing method (PNN)

show abstract

Detection of Brain Tumor in MRI Images by Applying Segmentation and Area Calculation Method Using SCILAB

Cited by 20 publications

References 3 publications

Comparative Study of Convolution Neural Network with GLCM Approach and Superpixel based Spectral Clustering for Brain Tumor Detection

Comparative Study of Convolution Neural Network with GLCM Approach and Superpixel based Spectral Clustering for Brain Tumor Detection

MAEU‐NET: A novel supervised architecture for brain tumor segmentation

Enhancement in Brain Image Segmentation using Swarm Ant Lion Algorithm

Contact Info

Product

Resources

About