scholarly journals Brain tumor segmentation based on deep learning and an attention mechanism using MRI multi-modalities brain images

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ramin Ranjbarzadeh ◽  
Abbas Bagherian Kasgari ◽  
Saeid Jafarzadeh Ghoushchi ◽  
Shokofeh Anari ◽  
Maryam Naseri ◽  
...  
Keyword(s):  
Deep Learning ◽  
Brain Tumor ◽  
Imaging Modality ◽  
Complex Structure ◽  
Computing Time ◽  
Tumor Segmentation ◽  
Global Features ◽  
Brain Tumor Segmentation ◽  
Brain Images ◽  
The Brain

AbstractBrain tumor localization and segmentation from magnetic resonance imaging (MRI) are hard and important tasks for several applications in the field of medical analysis. As each brain imaging modality gives unique and key details related to each part of the tumor, many recent approaches used four modalities T1, T1c, T2, and FLAIR. Although many of them obtained a promising segmentation result on the BRATS 2018 dataset, they suffer from a complex structure that needs more time to train and test. So, in this paper, to obtain a flexible and effective brain tumor segmentation system, first, we propose a preprocessing approach to work only on a small part of the image rather than the whole part of the image. This method leads to a decrease in computing time and overcomes the overfitting problems in a Cascade Deep Learning model. In the second step, as we are dealing with a smaller part of brain images in each slice, a simple and efficient Cascade Convolutional Neural Network (C-ConvNet/C-CNN) is proposed. This C-CNN model mines both local and global features in two different routes. Also, to improve the brain tumor segmentation accuracy compared with the state-of-the-art models, a novel Distance-Wise Attention (DWA) mechanism is introduced. The DWA mechanism considers the effect of the center location of the tumor and the brain inside the model. Comprehensive experiments are conducted on the BRATS 2018 dataset and show that the proposed model obtains competitive results: the proposed method achieves a mean whole tumor, enhancing tumor, and tumor core dice scores of 0.9203, 0.9113 and 0.8726 respectively. Other quantitative and qualitative assessments are presented and discussed.

scholarly journals Development of brain tumor segmentation of magnetic resonance imaging (MRI) using U-Net deep learning

2021 ◽  
Vol 4 (9(112)) ◽  
pp. 23-31
Author(s):  
Wasan M. Jwaid ◽  
Zainab Shaker Matar Al-Husseini ◽  
Ahmad H. Sabry
Keyword(s):  
Magnetic Resonance Imaging ◽  
Deep Learning ◽  
Brain Tumor ◽  
Magnetic Resonance ◽  
Tumor Segmentation ◽  
Resonance Imaging ◽  
Brain Tumor Segmentation ◽  
Brain Images ◽  
Magnetic Resonance Imaging Mri ◽  
The Brain

Brain tumors are the growth of abnormal cells or a mass in a brain. Numerous kinds of brain tumors were discovered, which need accurate and early detection techniques. Currently, most diagnosis and detection methods rely on the decision of neuro-specialists and radiologists to evaluate brain images, which may be time-consuming and cause human errors. This paper proposes a robust U-Net deep learning Convolutional Neural Network (CNN) model that can classify if the subject has a tumor or not based on Brain Magnetic resonance imaging (MRI) with acceptable accuracy for medical-grade application. The study built and trained the 3D U-Net CNN including encoding/decoding relationship architecture to perform the brain tumor segmentation because it requires fewer training images and provides more precise segmentation. The algorithm consists of three parts; the first part, the downsampling part, the bottleneck part, and the optimum part. The resultant semantic maps are inserted into the decoder fraction to obtain the full-resolution probability maps. The developed U-Net architecture has been applied on the MRI scan brain tumor segmentation dataset in MICCAI BraTS 2017. The results using Matlab-based toolbox indicate that the proposed architecture has been successfully evaluated and experienced for MRI datasets of brain tumor segmentation including 336 images as training data and 125 images for validation. This work demonstrated comparative performance and successful feasibility of implementing U-Net CNN architecture in an automated framework of brain tumor segmentations in Fluid-attenuated inversion recovery (FLAIR) MR Slices. The developed U-Net CNN model succeeded in performing the brain tumor segmentation task to classify the input brain images into a tumor or not based on the MRI dataset.

scholarly journals Brain Tumor Segmentation in MRI Images using Convolution Neural Networks

2019 ◽  
Vol 8 (4) ◽  
pp. 2051-2054
Keyword(s):  
Brain Tumor ◽  
Automatic Segmentation ◽  
Important Task ◽  
Tumor Segmentation ◽  
Brain Tumor Segmentation ◽  
Brain Images ◽  
Medical Issues ◽  
Current Scenario ◽  
Exact Position ◽  
The Brain

Medical image processing is an important task in current scenario as more and more humans are diagnosed with various medical issues. Brain tumor (BT) is one of the problems that is increasing at a rapid rate and its early detection is important in increasing the survival rate of humans. Detection of tumor from Magnetic Resonance Image (MRI) of brain is very difficult when done manually and also time consuming. Further the tumors assume different shapes and may be present in any portion of the brain. Hence identification of the tumor poses an important task in the lives of human and it is necessary to identify its exact position in the brain and the affected regions. The proposed algorithm makes use of deep learning concepts for automatic segmentation of the tumor from the MRI brain images. The algorithm is implemented using MATLAB and an accuracy of 99.1% is achieved.

scholarly journals MRI-based brain tumor segmentation using FPGA-accelerated neural network

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Siyu Xiong ◽  
Guoqing Wu ◽  
Xitian Fan ◽  
Xuan Feng ◽  
Zhongcheng Huang ◽  
...  
Keyword(s):  
Neural Network ◽  
Energy Efficiency ◽  
Deep Learning ◽  
Brain Tumor ◽  
Brain Tumors ◽  
Tumor Segmentation ◽  
Brain Tumor Segmentation ◽  
Cad Systems ◽  
Segmentation Accuracy ◽  
The Brain

Abstract Background Brain tumor segmentation is a challenging problem in medical image processing and analysis. It is a very time-consuming and error-prone task. In order to reduce the burden on physicians and improve the segmentation accuracy, the computer-aided detection (CAD) systems need to be developed. Due to the powerful feature learning ability of the deep learning technology, many deep learning-based methods have been applied to the brain tumor segmentation CAD systems and achieved satisfactory accuracy. However, deep learning neural networks have high computational complexity, and the brain tumor segmentation process consumes significant time. Therefore, in order to achieve the high segmentation accuracy of brain tumors and obtain the segmentation results efficiently, it is very demanding to speed up the segmentation process of brain tumors. Results Compared with traditional computing platforms, the proposed FPGA accelerator has greatly improved the speed and the power consumption. Based on the BraTS19 and BraTS20 dataset, our FPGA-based brain tumor segmentation accelerator is 5.21 and 44.47 times faster than the TITAN V GPU and the Xeon CPU. In addition, by comparing energy efficiency, our design can achieve 11.22 and 82.33 times energy efficiency than GPU and CPU, respectively. Conclusion We quantize and retrain the neural network for brain tumor segmentation and merge batch normalization layers to reduce the parameter size and computational complexity. The FPGA-based brain tumor segmentation accelerator is designed to map the quantized neural network model. The accelerator can increase the segmentation speed and reduce the power consumption on the basis of ensuring high accuracy which provides a new direction for the automatic segmentation and remote diagnosis of brain tumors.

scholarly journals Recognization and Systematization of MR Images using K Means Clustering and DNN

2020 ◽  
Vol 9 (6) ◽  
pp. 924-927
Keyword(s):  
Deep Learning ◽  
Brain Tumor ◽  
Brain Tumors ◽  
Digital Health ◽  
High Accuracy ◽  
Tumor Segmentation ◽  
Mr Images ◽  
Brain Tumor Segmentation ◽  
Brain Tumor Diagnosis ◽  
The Brain

Brain tumors are the result of unusual growth and unrestrained cell disunity in the brain. Most of the medical image application lack in segmentation and labeling. Brain tumors can lead to loss of lives if they are not detected early and correctly. Recently, deep learning has been an important role in the field of digital health. One of its action is the reduction of manual decision in the diagnosis of diseases specifically brain tumor diagnosis needs high accuracy, where minute errors in judgment may lead to loss therefore, brain tumor segmentation is an necessary challenge in medical side. In recent time numerous ,methods exist for tumor segmentation with lack of accuracy. Deep learning is used to achieve the goal of brain tumor segmentation. In this work, three network of brain MR images segmentation is employed .A single network is compared to achieve segmentation of MR images using separate network .In this paper segmentation has improved and result is obtained with high accuracy and efficiency.

scholarly journals Hybrid Architecture for Brain Abnormality Detection on brain MR Scans via FastAI

2021 ◽  
Author(s):  
Asmita Dixit
Keyword(s):  
Deep Learning ◽  
Brain Tumor ◽  
Transfer Learning ◽  
Tumor Classification ◽  
Tumor Segmentation ◽  
Hybrid Architecture ◽  
Brain Tumor Segmentation ◽  
Brain Images ◽  
Brain Abnormality ◽  
Computer Aided

Abstract With lot happening in the field of Deep Learning, classification of brain tumor is still a matter of concern. Brain tumor segmentation and classification using MRI scans has achieved lot of interest in the area of medical imaging. The emphasis still lies on developing automatic computer-aided system for early predictions and diagnosis. MRI of brain Tumors not only varies in shape but sometimes gives less contrasted details also. In this paper, we present a FastAI based Transfer Learning tumor classification in which pre-trained model with segmented features classifies tumor based on its learning. The proposed model with the technique of Deep learning applies ResNet152 as base model to extract features from the MRI brain images. With certain changes in the last 3 layers of ResNet152, 97% accuracy in Dataset-253, 96% accuracy in Dataset-205 is achieved. Models such as Resnet50, VGG16, ResNet34 and Basic CNN is also evaluated. The model improved from ResNet152 has provided improved results. The observations suggest that usage of Transfer Learning is effective when the Dataset is limited. The prepared model is effective and can be collaborated in computer-aided brain MR images Tumor classification.

A Systematic Review of Clustering and Classifier Techniques for Brain Tumor Segmentation in MRI Images

2021 ◽  
Vol 11 (2) ◽  
pp. 46-57
Author(s):  
Veeresh Ashok Mulimani ◽  
Sanjeev S. Sannakki ◽  
Vijay S. Rajpurohit
Keyword(s):  
Systematic Review ◽  
Brain Tumor ◽  
Clustering Algorithms ◽  
Tumor Segmentation ◽  
Brain Tumor Segmentation ◽  
Brain Images ◽  
Non Invasive ◽  
Tissue Contrast ◽  
High Resolution Images ◽  
The Brain

MRI technique is widely used in the field of medicine because of its high spatial resolution, non-invasive characteristics, and soft tissue contrast. In this review article, a systematic study has been conducted to analyze the performance and issues of various techniques for brain tumor segmentation. Latest research on BTS in MRI with the higher resolution is utilized for the systematic review. The high-resolution images increase execution time of the classification, and accuracy is the other problem in BTS. Still, there is some research lacking in accuracy on the brain segmentation. Few researchers carried out the classification of different kinds of tissues in the brain images and also on the prediction on growth of tumor. Each method has specific technique to improve the performance of the BTS, and these methods are compared with one another in terms of result. Research comparison helps to understand the proposed method with their achieved results. Clustering algorithms such as K-means and FCM are generally used for segmentation, and GA, ANN, ANFIS, FCNN, SVM are commonly used as classifiers.

scholarly journals Brain Tumor Segmentation using FCM and Symbolic Feature

2019 ◽  
Vol 8 (6S) ◽  
pp. 654-658
Keyword(s):  
Brain Tumor ◽  
Malignant Tumors ◽  
Median Filter ◽  
Tumor Segmentation ◽  
Brain Tumor Segmentation ◽  
Brain Images ◽  
Normal Tissues ◽  
Tumor Region ◽  
Mr Brain Images ◽  
The Brain

The brain tumor segmentation from image is interesting and challenging in the field of image processing and pattern recognition. An early detection of a brain tumor region helps the patient to take the correct medicine and increase the rate of the survival.The brain tumor segmentation is a process of differentiating the abnormal tissues and normal tissues. most common types of brain tumors are Benign and Malignant tumors. In this paper, the Fuzzy C-Means (FCM) approach is used to cluster the abnormal cells region and normal cells region in the brain image. The possible noises are removed by employing the median filter and morphological function is applied to extract the possible tumor region. The true tumor region is extracted with the help of symbolic features. Finally, the proposed methods is tested on T2- weighted MR brain images

scholarly journals Brain Tumor Segmentation using Normalized Graph Cuts

2019 ◽  
Vol 8 (6) ◽  
pp. 3159-3161
Keyword(s):  
Brain Tumor ◽  
Complex Structure ◽  
Weighted Graph ◽  
Graph Cut ◽  
Magnetic Resonance Images ◽  
Comprehensive Analysis ◽  
Tumor Segmentation ◽  
Brain Tumor Segmentation ◽  
Blood Clots ◽  
The Brain

Normalized graph cut algorithm is an efficient method where the technique of graph theory is adopted and in which the images are taken in the form of weighted graph in order to segment the images. This paper comprises of the fundamental concept of Normalized graph cut algorithm and its application towards the segmentation of Brain tumor. Identifying defects such as tumors is a very challenging because differentiating the components is difficult in a complex structure like a human brain. The diagnosis becomes even more complex because the tumor, blood clots and some part of the brain tissues appear as the same Brain tumor is generally detected and analyzed through a comprehensive analysis of the Magnetic Resonance Images of the brain. This technique gives a second opinion regarding the presence or absence of the brain tumor. This paper performs the study of Normalized graph cut algorithm and shows its efficiency in detecting tumors and compares it with other commonly used algorithms

Brain Tumor Segmentation and Prediction using Fuzzy Neighborhood Learning Approach for 3D MRI Images

2021 ◽  
Author(s):  
Pitchai R ◽  
Supraja P ◽  
Razia Sulthana A ◽  
Veeramakali T
Keyword(s):  
Deep Learning ◽  
Brain Tumor ◽  
Brain Tumors ◽  
Network Architecture ◽  
Tumor Detection ◽  
Tumor Segmentation ◽  
Brain Tumor Segmentation ◽  
3D Images ◽  
Learning Techniques ◽  
The Brain

Abstract Segmentation of brain tumors is a daunting process comprising the delineation of heterogeneous cancerous tissues and diffuse types in anatomical representations of the brain. Deep learning techniques have recently made important strides in the segmentation of brain tumors. However, owing to the irregularity of the tumor, most of the deep learning-based segmentation techniques are not used directly for tumor detection. Although recent studies are capable of addressing the irregularity issue and retaining permutation invariance, many approaches struggle to catch the valuable high-dimensional local features of finer resolution. Inspired by the fuzzy learning methods and an analysis of the shortcomings of existing methods, an automated fuzzy neighborhood learning-based 3D segmentation technique has been proposed for the detection of cerebrum tumors in 3D images. In this technique, the fuzzy neighborhood function is deeply integrated with the proposed network architecture. This technique has been evaluated on BRATS 2013dataset. The simulation results show that the proposed brain tumor detection technique is superior to other methods in the diagnosis of brain tumors with the dice coefficient of 0.85 and the Jaccard index of 0.74.

Sign in / Sign up

Export Citation Format

Share Document