Brain Tumor Detection from MR Images Employing Fuzzy Graph Cut Technique

2020 ◽  
Vol 13 (3) ◽  
pp. 362-369 ◽  
Author(s):  
Jyotsna Dogra ◽  
Shruti Jain ◽  
Ashutosh Sharma ◽  
Rajiv Kumar ◽  
Meenakshi Sood
Keyword(s):  
Performance Metrics ◽  
Brain Mri ◽  
Tumor Detection ◽  
Graph Cut ◽  
Tumor Segmentation ◽  
Low Grade ◽  
Seed Selection ◽  
Selection Technique ◽  
Kernel Selection ◽  
Mri Image

Background: This research aims at the accurate selection of the seed points from the brain MRI image for the detection of the tumor region. Since, the conventional way of manual seed selection leads to inappropriate tumor extraction therefore, fuzzy clustering technique is employed for the accurate seed selection for performing the segmentation through graph cut method. Methods: In the proposed method Fuzzy Kernel Seed Selection technique is used to define the complete brain MRI image into different groups of similar intensity. Among these groups the most accurate kernels are selected empirically that show highest resemblance with the tumor. The concept of fuzziness helps making the selection even at the boundary regions. Results: The proposed Fuzzy kernel selection technique is applied on the BraTS dataset. Among the four modalities, the proposed technique is applied on Flair images. This dataset consists of Low Grade Glioma (LGG) and High Grade Glioma (HGG) tumor images. The experiment is conducted on more than 40 images and validated by evaluating the following performance metrics: 1. Disc Similarity Coefficient (DSC), 2. Jaccard Index (JI) and 3. Positive Predictive Value (PPV). The mean DSC and PPV values obtained for LGG images are 0.89 and 0.87 respectively; and for HGG images it is 0.92 and 0.90 respectively. Conclusion: On comparing the proposed Fuzzy kernel selection graph cut technique approach with the existing techniques it is observed that the former provides an automatic accurate tumor detection. It is highly efficient and can provide a better performance for HGG and LGG tumor segmentation in clinical application.

IBRDM: An Intelligent Framework for Brain Tumor Classification Using Radiomics- and DWT-based Fusion of MRI Sequences

2022 ◽  
Vol 22 (1) ◽  
pp. 1-30
Author(s):  
Rahul Kumar ◽  
Ankur Gupta ◽  
Harkirat Singh Arora ◽  
Balasubramanian Raman
Keyword(s):  
Brain Tumor ◽  
Brain Tumors ◽  
Performance Metrics ◽  
Tumor Classification ◽  
Training Dataset ◽  
Tumor Segmentation ◽  
Discrete Wavelet ◽  
Low Grade ◽  
Brain Tumor Segmentation ◽  
Mri Sequences

Brain tumors are one of the critical malignant neurological cancers with the highest number of deaths and injuries worldwide. They are categorized into two major classes, high-grade glioma (HGG) and low-grade glioma (LGG), with HGG being more aggressive and malignant, whereas LGG tumors are less aggressive, but if left untreated, they get converted to HGG. Thus, the classification of brain tumors into the corresponding grade is a crucial task, especially for making decisions related to treatment. Motivated by the importance of such critical threats to humans, we propose a novel framework for brain tumor classification using discrete wavelet transform-based fusion of MRI sequences and Radiomics feature extraction. We utilized the Brain Tumor Segmentation 2018 challenge training dataset for the performance evaluation of our approach, and we extract features from three regions of interest derived using a combination of several tumor regions. We used wrapper method-based feature selection techniques for selecting a significant set of features and utilize various machine learning classifiers, Random Forest, Decision Tree, and Extra Randomized Tree for training the model. For proper validation of our approach, we adopt the five-fold cross-validation technique. We achieved state-of-the-art performance considering several performance metrics, 〈 Acc , Sens , Spec , F1-score , MCC , AUC 〉 ≡ 〈 98.60%, 99.05%, 97.33%, 99.05%, 96.42%, 98.19% 〉, where Acc , Sens , Spec , F1-score , MCC , and AUC represents the accuracy, sensitivity, specificity, F1-score, Matthews correlation coefficient, and area-under-the-curve, respectively. We believe our proposed approach will play a crucial role in the planning of clinical treatment and guidelines before surgery.

scholarly journals Brain MRI Images Segmentation Based on U-Net Architecture

2021 ◽  
Vol 18 (1) ◽  
pp. 21-27
Author(s):  
Assalah Atiyah ◽  
Khawla Ali
Keyword(s):  
Brain Tumor ◽  
Brain Tumors ◽  
Performance Metrics ◽  
Brain Mri ◽  
Regular Function ◽  
Survival Rates ◽  
Tumor Segmentation ◽  
Brain Tumor Segmentation ◽  
Edge Based ◽  
The Brain

Brain tumors are collections of abnormal tissues within the brain. The regular function of the brain may be affected as it grows within the region of the skull. Brain tumors are critical for improving treatment options and patient survival rates to prevent and treat them. The diagnosis of cancer utilizing manual approaches for numerous magnetic resonance imaging (MRI) images is the most complex and time-consuming task. Brain tumor segmentation must be carried out automatically. A proposed strategy for brain tumor segmentation is developed in this paper. For this purpose, images are segmented based on region-based and edge-based. Brain tumor segmentation 2020 (BraTS2020) dataset is utilized in this study. A comparative analysis of the segmentation of images using the edge-based and region-based approach with U-Net with ResNet50 encoder, architecture is performed. The edge-based segmentation model performed better in all performance metrics compared to the region-based segmentation model and the edge-based model achieved the dice loss score of 0. 008768, IoU score of 0. 7542, f1 score of 0. 9870, the accuracy of 0. 9935, the precision of 0. 9852, recall of 0. 9888, and specificity of 0. 9951.

Brain Tumor Detection Using Multipath Convolution Neural Network (CNN)

2020 ◽  
Vol 10 (4) ◽  
pp. 43-53
Author(s):  
Mukesh Kumar Chandrakar ◽  
Anup Mishra
Keyword(s):  
Neural Network ◽  
Brain Tumor ◽  
Performance Metrics ◽  
Research Problem ◽  
Tumor Detection ◽  
Convolution Neural Network ◽  
Detection Methods ◽  
Tumor Segmentation ◽  
Brain Tumor Segmentation ◽  
Medical Image Diagnosis

Brain tumor segmentation is an emerging application of automated medical image diagnosis. Robust approach of brain tumor segmentation and detection is a research problem, and the performance metrics of the existing tumor detection methods are not appropriately known. Deep neural network using convolution neural network (CNN) is being researched in this direction, but no general architecture is found that can be used as robust method for brain tumor detection. The authors have proposed a multipath CNN architecture for brain tumor segmentation and detection, which provides improved results as compared to existing methods. The proposed work has been tested for datasets BRATS2013, BRTAS2015, and BRATS2017 with significant improvement in dice index and timing values by utilizing the capability of multipath CNN architecture, which combines both local and global paths.

Brain MR Image Classification for Glioma Tumor detection using Deep Convolutional Neural Network Features

2020 ◽  
Vol 16 ◽  
Author(s):  
Ghazanfar Latif ◽  
D.N.F. Awang Iskandar ◽  
Jaafar Alghazo ◽  
M. Mohsin Butt
Keyword(s):  
Brain Tumor ◽  
Image Classification ◽  
High Grade Glioma ◽  
Tumor Detection ◽  
Tumor Classification ◽  
Tumor Segmentation ◽  
Low Grade ◽  
Mr Images ◽  
Mr Image ◽  
Glioma Tumor

Background: Detection of brain tumor is a complicated task which requires specialized skills and interpretation techniques. Accurate brain tumor classification and segmentation from MR images provide an essential choice for medical treatments. The different objects within an MR image have similar size, shape, and density which makes the tumor classification and segmentation even more complex. Objectives: Classification of the brain MR images into tumorous and non-tumorous using deep features and different classifiers to get higher accuracy. Methods: In this study, a novel four-step process is proposed; pre-processing for image enhancement and compression, feature extraction using convolutional neural networks (CNN), classification using the multilayer perceptron and finally, tumor segmentation using enhanced fuzzy c-means method. Results: The system is tested on 65 cases in four modalities consisting of 40,300 MR Images obtained from the BRATS-2015 dataset. These include images of 26 Low-Grade Glioma (LGG) tumor cases and 39 High-Grade Glioma (HGG) tumor cases. The proposed CNN features-based classification technique outperforms the existing methods by achieving an average accuracy of 98.77% and a noticeable improvement in the segmentation results are measured. Conclusion: The proposed method for brain MR image classification to detect Glioma Tumor detection can be adopted as it gives better results with high accuracies.

scholarly journals Application of Machine Learning and Deep Learning Methods for Brain Tumor Identification and Classification

2020 ◽  
Vol 8 (6) ◽  
pp. 2886-2891
Keyword(s):  
Machine Learning ◽  
Image Segmentation ◽  
Deep Learning ◽  
Brain Tumor ◽  
Brain Mri ◽  
Tumor Segmentation ◽  
Survival Ratio ◽  
Reliable Technique ◽  
Tumor Identification ◽  
Mri Image

In the area of medical imaging technology, advances in Artificial intelligence (AI) delivers promising solutions with higher accuracy. For healthcare solutions, medical images provides a systematic way for diagnosis the diseases earlier and make treatments more effective. Machine learning and deep learning are rapidly grown fields of AI that may apply to many domains including image processing, speech recognition and text understanding. As MRI image segmentation is a key task for identification of brain anomalies, a fast and reliable technique is essential for increasing the survival ratio of affected patients. Manual segmentation of the brain MRI image involves more time and it may subject to inaccuracies. Hence, AI approaches and algorithms have been developed for tumor segmentation. This paper contains the detailed study of the available methods of machine learning and deep learning for brain tumor identification and classification through MRI image segmentation. It discusses and summarizes the methodologies and its results available for classification of brain tumor.

Fuzzy Corner Metric Based Brain Tumor Segmentation And Grade Classification

2019 ◽  
Vol 14 ◽  
Author(s):  
Kalyana Sundaram ◽  
J. Gnanavadivel
Keyword(s):  
Brain Mri ◽  
Main Idea ◽  
Tumor Detection ◽  
Support Vector ◽  
Tumor Segmentation ◽  
Brain Tumor Segmentation ◽  
Grade Classification ◽  
Physician Time ◽  
Tumors Classification

Background: The main idea this work is to reduce the physician time by assessing with computer aided tumor detection Human investigation is the routine technique for brain MRI tumor detection and tumors classification. Interpretation of images is based on organized and explicit classification of brain MRI and also various techniques have been proposed. Methods: The proposed system segments the Brain MR images with the help of fuzzy corner points and the segmented tumor area is subjected for grade classification. Support vector machine classifier is implemented for the grade classification. Result & Conclusion: The proposed system is addressing a simple and robust technique for the tumor diagnosis and also the performance is 89.5% which is comparatively high when compared to the traditional classification method.

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