Brain Tumor Segmentation Using 3D Convolutional Neural Network

2020 ◽  
pp. 199-207
Author(s):  
Kaisheng Liang ◽  
Wenlian Lu
Keyword(s):  
Neural Network ◽  
Brain Tumor ◽  
Convolutional Neural Network ◽  
Tumor Segmentation ◽  
Brain Tumor Segmentation

scholarly journals Brain Tumor Segmentation using K-Means Clustering and Detection using Convolutional Neural Network

2020 ◽  
Vol 9 (5) ◽  
pp. 1452-1455
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Brain Tumor ◽  
Convolutional Neural Network ◽  
Clustering Algorithm ◽  
Tumor Segmentation ◽  
Brain Tumor Segmentation ◽  
Mri Scans ◽  
Two Phases ◽  
Processing Techniques

This paper presents brain tumor detection and segmentation using image processing techniques. Convolutional neural networks can be applied for medical research in brain tumor analysis. The tumor in the MRI scans is segmented using the K-means clustering algorithm which is applied of every scan and the feed it to the convolutional neural network for training and testing. In our CNN we propose to use ReLU and Sigmoid activation functions to determine our end result. The training is done only using the CPU power and no GPU is used. The research is done in two phases, image processing and applying neural network.

scholarly journals An Intelligent Diagnosis Method of Brain MRI Tumor Segmentation Using Deep Convolutional Neural Network and SVM Algorithm

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Wentao Wu ◽  
Daning Li ◽  
Jiaoyang Du ◽  
Xiangyu Gao ◽  
Wen Gu ◽  
...  
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Brain Tumor ◽  
Convolutional Neural Network ◽  
Deep Convolutional Neural Network ◽  
Support Vector ◽  
Tumor Segmentation ◽  
Brain Segmentation ◽  
Brain Tumor Segmentation ◽  
Segmentation Methods

Among the currently proposed brain segmentation methods, brain tumor segmentation methods based on traditional image processing and machine learning are not ideal enough. Therefore, deep learning-based brain segmentation methods are widely used. In the brain tumor segmentation method based on deep learning, the convolutional network model has a good brain segmentation effect. The deep convolutional network model has the problems of a large number of parameters and large loss of information in the encoding and decoding process. This paper proposes a deep convolutional neural network fusion support vector machine algorithm (DCNN-F-SVM). The proposed brain tumor segmentation model is mainly divided into three stages. In the first stage, a deep convolutional neural network is trained to learn the mapping from image space to tumor marker space. In the second stage, the predicted labels obtained from the deep convolutional neural network training are input into the integrated support vector machine classifier together with the test images. In the third stage, a deep convolutional neural network and an integrated support vector machine are connected in series to train a deep classifier. Run each model on the BraTS dataset and the self-made dataset to segment brain tumors. The segmentation results show that the performance of the proposed model is significantly better than the deep convolutional neural network and the integrated SVM classifier.

scholarly journals Emergence of Convolutional Neural Network in Future Medicine: Why and How. A Review on Brain Tumor Segmentation

2018 ◽  
Vol 24 (1) ◽  
pp. 43-53
Author(s):  
Behrouz Alizadeh Savareh ◽  
Hassan Emami ◽  
Mohamadreza Hajiabadi ◽  
Mahyar Ghafoori ◽  
Seyed Majid Azimi
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Brain Tumor ◽  
Convolutional Neural Network ◽  
Magnetic Resonance Images ◽  
Tumor Segmentation ◽  
Brain Tumor Segmentation ◽  
Manual Analysis ◽  
The Brain ◽  
Evaluation Parameters

Abstract Manual analysis of brain tumors magnetic resonance images is usually accompanied by some problem. Several techniques have been proposed for the brain tumor segmentation. This study will be focused on searching popular databases for related studies, theoretical and practical aspects of Convolutional Neural Network surveyed in brain tumor segmentation. Based on our findings, details about related studies including the datasets used, evaluation parameters, preferred architectures and complementary steps analyzed. Deep learning as a revolutionary idea in image processing, achieved brilliant results in brain tumor segmentation too. This can be continuing until the next revolutionary idea emerging.

Cascading handcrafted features and Convolutional Neural Network for IoT-enabled brain tumor segmentation

2020 ◽  
Vol 153 ◽  
pp. 196-207 ◽  
Author(s):  
Hikmat Khan ◽  
Pir Masoom Shah ◽  
Munam Ali Shah ◽  
Saif ul Islam ◽  
Joel J.P.C. Rodrigues
Keyword(s):  
Neural Network ◽  
Brain Tumor ◽  
Convolutional Neural Network ◽  
Tumor Segmentation ◽  
Brain Tumor Segmentation
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