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.

An Automated Brain Tumor Detection in MRI using Firefly Optimized Segmentation

2017 ◽  
Vol 7 (8) ◽  
pp. 290
Author(s):  
Prabhjot Kaur ◽  
Amardeep Kaur
Keyword(s):  
Brain Tumor ◽  
Firefly Algorithm ◽  
Tumor Detection ◽  
Poor Quality ◽  
Tumor Segmentation ◽  
Mr Images ◽  
Medical Field ◽  
Mr Image ◽  
Brain Tumor Segmentation ◽  
Localize Tumor

In the medical field brain tumor detection is an important application. The existing techniques of segmentation has various limitations. Existing techniques ignored the medical images which have poor quality or low brightness. Segmentation becomes the challenging issue as the image contains non-uniform object texture, cluttered objects, different image content and image noise. New technique of segmentation is proposed by research to detect tumor from MR images using firefly algorithm, then tumor is segmented and its features are extracted from MR image.  The main goal of Research to design an algorithm for MRI based brain tumor segmentation using firefly algorithm and to improve the accuracy of the tumor detection. Fireflies produce a reaction in their body which produce light , this chemical reaction is called bioluminescent. By using firefly technique it is possible to detect and localize tumor accurately. For comparative analysis, various parameters are used to demonstrate the superiority of proposed method over the conventional ones.

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 Reliability of Segmenting Brain Tumor and Finding Optimal Volume Estimator for MR Images of Patients with Glioma’s

2019 ◽  
Vol 8 (9) ◽  
pp. 1647-1652
Keyword(s):  
Brain Tumor ◽  
Tumor Volume ◽  
Region Growing ◽  
Threshold Value ◽  
Volume Estimation ◽  
Clinical Aspects ◽  
Tumor Segmentation ◽  
Low Grade ◽  
Seed Point ◽  
Glioma Tumor

Tumor volume estimation is a significant prognostic part of the Glioma tumor detection. Reliable assessment of Glioma tumor segmentation and volume estimation is a common problem in clinical aspects. We aim to propose a tumor segmentation method by suggesting suitable estimator for MR brain tumor volume construction. Run length algorithm is used to automatic initialize the seed point to the region growing algorithm. Region growing algorithm works with a threshold value using 8 × 8 patches. In this experiment includes thirty BraTS2013 high-grade and low-grade Glioma datasets. Proposed method yield 80.12% of Dice similarity with 6.8% of deviation and 84% of accuracy with 10% of deviation. The proposed work uses six state-of-the-art volume detectors to estimate the size of tumor volume. From the results, Cavalieri’s estimator gives more accurate results with less deviation

scholarly journals A Review on Brain Tumor Classification Methodologies

2019 ◽  
pp. 346-359 ◽  
Author(s):  
Bichitra Panda ◽  
Chandra Sekhar Panda
Keyword(s):  
Feature Extraction ◽  
Brain Tumor ◽  
Brain Tumors ◽  
Brain Mri ◽  
Tumor Classification ◽  
Low Grade ◽  
High Grade ◽  
Mr Images ◽  
The Brain

Brain tumor is one of the leading disease in the world. So automated identification and classification of tumors are important for diagnosis. Magnetic resonance imaging (MRI)is widely used modality for imaging brain. Brain tumor classification refers to classify the brain MR images as normal or abnormal, benign or malignant, low grade or high grade or types. This paper reviews various techniques used for the classification of brain tumors from MR images. Brain tumor classification can be divided into three phases as preprocessing, feature extraction and classification. As segmentation is not mandatory for classification, hence resides in the first phase. The feature extraction phase also contains feature reduction. DWT is efficient for both preprocessing and feature extraction. Texture analysis based on GLCM gives better features for classification where PCA reduces the feature vector maintaining the accuracy of classification of brain MRI. Shape features are important where segmentation has already been performed. The use of SVM along with appropriate kernel techniques can help in classifying the brain tumors from MRI. High accuracy has been achieved to classify brain MRI as normal or abnormal, benign or malignant and low grade or high grade. But classifying the tumors into more particular types is more challenging.

scholarly journals Bidirectional ConvLSTMXNet for Brain Tumor Segmentation of MR Images

2021 ◽  
Vol 15 (1) ◽  
pp. 37-42
Author(s):  
M. Ravikumar ◽  
B.J. Shivaprasad
Keyword(s):  
Deep Learning ◽  
Brain Tumor ◽  
Tumor Segmentation ◽  
Mr Images ◽  
Mr Image ◽  
Brain Tumor Segmentation ◽  
Data Set ◽  
Tumour Segmentation ◽  
Brain Tumour Segmentation

In recent years, deep learning based networks have achieved good performance in brain tumour segmentation of MR Image. Among the existing networks, U-Net has been successfully applied. In this paper, it is propose deep-learning based Bidirectional Convolutional LSTM XNet (BConvLSTMXNet) for segmentation of brain tumor and using GoogLeNet classify tumor & non-tumor. Evaluated on BRATS-2019 data-set and the results are obtained for classification of tumor and non-tumor with Accuracy: 0.91, Precision: 0.95, Recall: 1.00 & F1-Score: 0.92. Similarly for segmentation of brain tumor obtained Accuracy: 0.99, Specificity: 0.98, Sensitivity: 0.91, Precision: 0.91 & F1-Score: 0.88.

scholarly journals Brain Tumor Detection and Identification Using Histogram Method

2019 ◽  
Vol 8 (10) ◽  
pp. 3517-3521
Keyword(s):  
Brain Tumor ◽  
Human Brain ◽  
Tumor Detection ◽  
Mr Images ◽  
Histogram Method ◽  
Mr Image ◽  
Image Scanning ◽  
Detection And Identification ◽  
Different Parts

Brain tumor is a collection of unwanted cells that grow abnormally in different parts of human brain. Detection of this is done effectively by MR image scanning of human brain. Certain process can be carried out to partition the input MR images into the regions. To convert these regions into coherent segments is done by Histogram Method which can utilize peaks and valleys to analyze the regions into segment of the MR images. This process can be done by program division method to detect the tumor in the earlier stages .This work aims at it.

scholarly journals Does Anatomical Contextual Information Improve 3D U-Net-Based Brain Tumor Segmentation?

Diagnostics ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 1159
Author(s):  
Iulian Emil Tampu ◽  
Neda Haj-Hosseini ◽  
Anders Eklund
Keyword(s):  
Brain Tumor ◽  
Contextual Information ◽  
Tumor Segmentation ◽  
Low Grade ◽  
Mr Image ◽  
Brain Tumor Segmentation ◽  
Training Time ◽  
Probability Maps ◽  
Segmentation Accuracy ◽  
Model Training

Effective, robust, and automatic tools for brain tumor segmentation are needed for the extraction of information useful in treatment planning. Recently, convolutional neural networks have shown remarkable performance in the identification of tumor regions in magnetic resonance (MR) images. Context-aware artificial intelligence is an emerging concept for the development of deep learning applications for computer-aided medical image analysis. A large portion of the current research is devoted to the development of new network architectures to improve segmentation accuracy by using context-aware mechanisms. In this work, it is investigated whether or not the addition of contextual information from the brain anatomy in the form of white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) masks and probability maps improves U-Net-based brain tumor segmentation. The BraTS2020 dataset was used to train and test two standard 3D U-Net (nnU-Net) models that, in addition to the conventional MR image modalities, used the anatomical contextual information as extra channels in the form of binary masks (CIM) or probability maps (CIP). For comparison, a baseline model (BLM) that only used the conventional MR image modalities was also trained. The impact of adding contextual information was investigated in terms of overall segmentation accuracy, model training time, domain generalization, and compensation for fewer MR modalities available for each subject. Median (mean) Dice scores of 90.2 (81.9), 90.2 (81.9), and 90.0 (82.1) were obtained on the official BraTS2020 validation dataset (125 subjects) for BLM, CIM, and CIP, respectively. Results show that there is no statistically significant difference when comparing Dice scores between the baseline model and the contextual information models (p > 0.05), even when comparing performances for high and low grade tumors independently. In a few low grade cases where improvement was seen, the number of false positives was reduced. Moreover, no improvements were found when considering model training time or domain generalization. Only in the case of compensation for fewer MR modalities available for each subject did the addition of anatomical contextual information significantly improve (p < 0.05) the segmentation of the whole tumor. In conclusion, there is no overall significant improvement in segmentation performance when using anatomical contextual information in the form of either binary WM, GM, and CSF masks or probability maps as extra channels.

A Survey on Detection and Classification of Brain Tumor Using Image Processing Techniques

2020 ◽  
pp. 967-973
Author(s):  
V. Deepika ◽  
T. Rajasenbagam
Keyword(s):  
Image Processing ◽  
Brain Tumor ◽  
Soft Tissues ◽  
Tumor Detection ◽  
Tumor Classification ◽  
Normal Brain ◽  
Image Processing Techniques ◽  
Normal Brain Function ◽  
Processing Techniques ◽  
The Brain

A brain tumor is an uncontrolled growth of abnormal brain tissue that can interfere with normal brain function. Although various methods have been developed for brain tumor classification, tumor detection and multiclass classification remain challenging due to the complex characteristics of the brain tumor. Brain tumor detection and classification are one of the most challenging and time-consuming tasks in the processing of medical images. MRI (Magnetic Resonance Imaging) is a visual imaging technique, which provides a information about the soft tissues of the human body, which helps identify the brain tumor. Proper diagnosis can prevent a patient's health to some extent. This paper presents a review of various detection and classification methods for brain tumor classification using image processing techniques.

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