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Brain Tumor Analysis Empowered with Deep Learning: A Review, Taxonomy, and Future Challenges

Muhammad Waqas Nadeem, Mohammed A. Al Ghamdi, Muzammil Hussain, Muhammad Adnan Khan, Khalid Masood Khan, Sultan H. Almotiri, Suhail Ashfaq Butt
2020 Brain Sciences  
Considering the wide range of applications of deep learning, the objective of this article is to review major deep learning concepts pertinent to brain tumor analysis (e.g., segmentation, classification  ...  A review conducted by summarizing a large number of scientific contributions to the field (i.e., deep learning in brain tumor analysis) is presented in this study.  ...  Convolutional Neural Network uses an encoder and decoder network with a singular hourglass structure for segmentation of the tumor region.  ... 
doi:10.3390/brainsci10020118 pmid:32098333 pmcid:PMC7071415 fatcat:wofq4puvcbemlconbz6carsf2y

TRIPLANAR CONVOLUTIONAL NEURAL NETWORK APPROACH FOR LIVER TUMOR SEGMENTATION

Sheng Hung Chung, Keng Hoon Gan, Anusha Achuthan, Rajeswari Mandava
2019 Zenodo  
The preliminary results show that Triplanar Convolutional Neural Network approach has better performance than Single-view Convolutional Neural Network approach in liver tumor segmentation.  ...  In this study, an automatic method based on Triplanar Convolutional Neural Network is proposed for liver tumor segmentation using Computed Tomography (CT) images.  ...  Triplanar Convolutional Neural Network method has been explored for medical image segmentation task including knee cartilage segmentation [8] and anatomical human brain segmentation [9] .  ... 
doi:10.5281/zenodo.3474224 fatcat:vqzoutvet5etjh4brjzbtaaa5u

Deep Learning Based Brain Tumor Segmentation: A Survey [article]

Zhihua Liu, Lei Tong, Zheheng Jiang, Long Chen, Feixiang Zhou, Qianni Zhang, Xiangrong Zhang, Yaochu Jin, Huiyu Zhou
2021 arXiv   pre-print
A number of deep learning based methods have been applied to brain tumor segmentation and achieved promising results.  ...  Brain tumor segmentation is one of the most challenging problems in medical image analysis. The goal of brain tumor segmentation is to generate accurate delineation of brain tumor regions.  ...  [19] reported a survey focusing on the use of deep convolutional neural networks for brain image analysis. This survey only highlights the usage of deep convolutional neural networks.  ... 
arXiv:2007.09479v3 fatcat:vdbpwfdsorfudkvnvottexd7je

Segmenting Brain Tumors with Symmetry [article]

Hejia Zhang, Xia Zhu, Theodore L. Willke
2017 arXiv   pre-print
We explore encoding brain symmetry into a neural network for a brain tumor segmentation task.  ...  We propose a method to encode brain symmetry into existing neural networks and apply the method to a state-of-the-art neural network for medical imaging segmentation.  ...  In this work, we propose a method to encode the brain symmetry into neural networks for brain tumor segmentation.  ... 
arXiv:1711.06636v1 fatcat:dcphsmcue5clrg6rxpx7z736v4

A Hybrid Convolutional Neural Network and Deep Belief Network for Brain Tumor Detection in MR Images

2019 International journal of recent technology and engineering  
For early detection or prediction of the brain tumor, an improved feature extraction technique along with Deep Neural Network (DNN) has been recommended.  ...  By integrating DCNN(Deep Convolutional Neural Network) classifier and DBN(Deep Belief Network), an improvement in precision rate can be achieved.  ...  Using CNN, S.Somasundaram, R.Gobinath A Hybrid Convolutional Neural Network and Deep Belief Network for Brain Tumor Detection in MR Images the feature is extracted by convolution process.  ... 
doi:10.35940/ijrte.b1193.0782s419 fatcat:wgzytnphlbeqlc5fyfswllkcqi

Advances on Tumor Image Segmentation Based on Artificial Neural Network

Shaohua Wang, Jianli Jiang, Xiaobing Lu
2020 Journal of Biosciences and Medicines  
This paper summarizes the advances of image segmentation by using artificial neural network including mainly the BP network and convolutional neural network (CNN).  ...  Many CNN models with different structures have been built and successfully used in segmentation of tumor images such as supervised and unsupervised learning CNN.  ...  However, the public data set has only limited data and so is difficult to train a network with Application of Artificial Neural Network in Tumor Segmentation Brain Tumor Segmentation The brain tumor  ... 
doi:10.4236/jbm.2020.87006 fatcat:tgxxvp6ecbf7dkzf5ubesnbnnu

The Multi Stage U-net Design for Brain Tumor Segmentation using Deep Learning Architecture

2020 International journal of recent technology and engineering  
The neural network is competent of end to end multi modal brain tumor segmentations.The Brain tumor segments are divided three categories.  ...  In this method validating with BraTS 2019 dataset and identify the test time enhancement improves the Brain tumor segmentation accurate images.  ...  The Manual segmentation of the brain tumor takes more time and accuracy of the image less, So now a days we are using deep learning method using automatic Brain tumor segmentation to identify the tumor  ... 
doi:10.35940/ijrte.c4531.099320 fatcat:qtyh6mesencr5g6tqpecdidpea

Multi-Task Deep Supervision on Attention R2U-Net for Brain Tumor Segmentation

Shiqiang Ma, Jijun Tang, Fei Guo
2021 Frontiers in Oncology  
APR is suitable for a deep learning model to help the network locate the tumor area accurately.  ...  Compared with the state-of-the-art brain tumor segmentation methods, our method has the characteristics of a small parameter and low computational cost.  ...  With the development of convolutional neural networks, the brain tumor automatic segmentation technology based on deep learning had achieved a high segmentation accuracy.  ... 
doi:10.3389/fonc.2021.704850 pmid:34604039 pmcid:PMC8484917 fatcat:ajjjhte4tfes7clnwxv3zstd4u

Front Matter: Volume 10134

2017 Medical Imaging 2017: Computer-Aided Diagnosis  
These two-number sets start with 00, 01, 02, 03, 04,  ...  for segmentation of brain tumors: Can we train with images from different institutions?  ...  04 Bladder cancer treatment response assessment using deep learning in CT with transfer learning 10134 05 Convolutional neural network based deep-learning architecture for prostate cancer detection on  ... 
doi:10.1117/12.2277119 dblp:conf/micad/X17 fatcat:ika7pheqxngdxejyvkss4dkbv4

An Efficient Brain Tumor Image Segmentation Based on Deep Residual Networks (ResNets)

Lamia H. Shehab, Omar M. Fahmy, Safa M. Gasser, Mohamed S. El-Mahallawy
2020 Journal of King Saud University: Engineering Sciences  
Many proposals investigate the use of Deep Neural Networks (DNN) in image segmentation as they have a high performance in automatic segmentation of brain tumors images.  ...  In this paper, we present an automatic technique for brain tumor segmentation depending on Deep Residual Learning Network (ResNet) to get over the gradient problem of DNN.  ...  Deep Neural Network Brain Tumor Segmentation Deep Neural Networks (DNNs) are very successful in extracting the full brain tumor and intra-tumor regions automatically.  ... 
doi:10.1016/j.jksues.2020.06.001 fatcat:42hu4jpd25ftxfz6ybe5qw33xe

A Modified Memory-Efficient U-Net for Segmentation of Polyps

Asif Ahmad, Department of Basic Sciences, University of Engineering and Technology Peshawar, Pakistan, Noor Badshah, Mahmood Ul Hassan, Department of Basic Sciences, University of Engineering and Technology Peshawar, Pakistan, Department of Basic Sciences, University of Engineering and Technology Peshawar, Pakistan
2021 International Journal of Engineering Works  
In this paper, we present an end-to-end deep neural network for segmentation of polyps in images. The network is modified version of the U-Net architecture.  ...  Nowadays, many supervised and unsupervised techniques are used for the task of segmentation. Deep neural networks have outperformed other state-of-the-art approaches for the task.  ...  CONCLUSION We presented a deep memory efficient neural network for segmentation of polyps.  ... 
doi:10.34259/ijew.21.804132137 fatcat:dsj2eu7v6bdmhozy2euanpqjra

3-D Convolutional Neural Networks for Glioblastoma Segmentation [article]

Darvin Yi and Mu Zhou and Zhao Chen and Olivier Gevaert
2016 arXiv   pre-print
Convolutional Neural Networks (CNN) have emerged as powerful tools for learning discriminative image features.  ...  We evaluate segmentation performance on the BRATS segmentation dataset with 274 tumor samples.  ...  Methods 3-D Convolutional Neural Networks Architecture Our CNN architecture utilizes 5 convolutional layers ( Figure 1 ).  ... 
arXiv:1611.04534v1 fatcat:3dbqdfeum5a2vfpztr7ll6bg3m

Brain Tumor Segmentation using Multi-View Attention based Ensemble Network

Noreen Mushtaq, Arfat Ahmad Khan, Faizan Ahmed Khan, Muhammad Junaid Ali, Malik Muhammad Ali Shahid, Chitapong Wechtaisong, Peerapong Uthansakul
2022 Computers Materials & Continua  
Deep learning-based brain tumor segmentation techniques proved best among available segmentation techniques.  ...  The proposed approach achieves Dice Similarity Score (DSC) of 0.77 on Enhancing Tumor (ET), 0.90 on Whole Tumor (WT), and 0.84 on Tumor Core (TC) with reduced Hausdorff Distance (HD) of 3.05 on ET, 5.12  ...  In [9] , for the task of brain tumor segmentation, a deep convolutional symmetric neural network is suggested.  ... 
doi:10.32604/cmc.2022.024316 fatcat:7bv7jktebnc5jovca3jh4q3ywu

Brain Tumor Segmentation through Level Based Learning Model

K. Dinesh Babu, C. Senthil Singh
2023 Computer systems science and engineering  
In this view, Convolutional Neural Networks deliver suitable models for efficient segmentation and thus delivered promising results.  ...  Tumors tend to be smaller in size and shape during their premature stages and they can easily evade the algorithms of Convolutional Neural Network (CNN).  ...  Extensively, machine learning has been substituted by deep learning models, convolutional neural networks for betterments with respect to accuracy and reliability.  ... 
doi:10.32604/csse.2023.024295 fatcat:kcibq53vajcdbkbvhvjwfmo76e

Deep Neural Networks for Medical Image Segmentation

Priyanka Malhotra, Sheifali Gupta, Deepika Koundal, Atef Zaguia, Wegayehu Enbeyle, Chinmay Chakraborty
2022 Journal of Healthcare Engineering  
This work presents a review of the literature in the field of medical image segmentation employing deep convolutional neural networks.  ...  Medical image segmentation is really a challenging task due to the various artefacts present in the images. Recently, deep neural models have shown application in various image segmentation tasks.  ...  Convolutional Neural Network.  ... 
doi:10.1155/2022/9580991 pmid:35310182 pmcid:PMC8930223 fatcat:oylwslatk5bcpocg45ro32shbq
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