Semantic Segmentation Using Deep Learning for Brain Tumor MRI via Fully Convolution Neural Networks

2018 ◽  
pp. 11-19 ◽  
Author(s):  
Sanjay Kumar ◽  
Ashish Negi ◽  
J. N. Singh
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Brain Tumor ◽  
Semantic Segmentation ◽  
Convolution Neural Networks ◽  
Tumor Mri

Deep Learning Techniques for Prediction, Detection, and Segmentation of Brain Tumors

2020 ◽  
pp. 118-154
Author(s):  
Prisilla Jayanthi ◽  
Muralikrishna Iyyanki
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Learning ◽  
Brain Tumor ◽  
Color Space ◽  
Image Data ◽  
Semantic Segmentation ◽  
Training Image ◽  
Hsv Color Space ◽  
Learning Techniques

In deep learning, the main techniques of neural networks, namely artificial neural network, convolutional neural network, recurrent neural network, and deep neural networks, are found to be very effective for medical data analyses. In this chapter, application of the techniques, viz., ANN, CNN, DNN, for detection of tumors in numerical and image data of brain tumor is presented. First, the case of ANN application is discussed for the prediction of the brain tumor for which the disease symptoms data in numerical form is the input. ANN modelling was implemented for classification of human ethnicity. Next the detection of the tumors from images is discussed for which CNN and DNN techniques are implemented. Other techniques discussed in this study are HSV color space, watershed segmentation and morphological operation, fuzzy entropy level set, which are used for segmenting tumor in brain tumor images. The FCN-8 and FCN-16 models are used to produce a semantic segmentation on the various images. In general terms, the techniques of deep learning detected the tumors by training image dataset.

scholarly journals Convolutional Neural Network for the Semantic Segmentation of Remote Sensing Images

2021 ◽  
Vol 26 (1) ◽  
pp. 200-215
Author(s):  
Muhammad Alam ◽  
Jian-Feng Wang ◽  
Cong Guangpei ◽  
LV Yunrong ◽  
Yuanfang Chen
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Neural Networks ◽  
Image Processing ◽  
Deep Learning ◽  
Semantic Segmentation ◽  
Natural Scene ◽  
Remote Sensing Images ◽  
Advantages And Disadvantages ◽  
Target Segmentation

AbstractIn recent years, the success of deep learning in natural scene image processing boosted its application in the analysis of remote sensing images. In this paper, we applied Convolutional Neural Networks (CNN) on the semantic segmentation of remote sensing images. We improve the Encoder- Decoder CNN structure SegNet with index pooling and U-net to make them suitable for multi-targets semantic segmentation of remote sensing images. The results show that these two models have their own advantages and disadvantages on the segmentation of different objects. In addition, we propose an integrated algorithm that integrates these two models. Experimental results show that the presented integrated algorithm can exploite the advantages of both the models for multi-target segmentation and achieve a better segmentation compared to these two models.

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