Proceedings ArticleDOI
Segmentation of Brain Tumour in MR Images Using Modified Deep Learning Network
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TLDR
In this paper, a modified segmentation network for brain tumour segmentation in Magnetic Resonance Images was presented. But, the network was not trained with other datasets and showed a good improvement in the results when tested on real-time MRI datasets.Abstract:
This paper presents a modified segmentation network for brain tumour segmentation in Magnetic Resonance Images. The early detection of brain tumour is quite mandatory for planning the treatment. This work proposes a computer-based automatic approach for the segmentation of brain tumour. The network proposed in this paper effectively delineated the boundaries of the brain tumour region. Exceedingly good results were obtained when the trained network was fed with other datasets. The network also showed a good improvement in the results when it was tested on real-time MRI datasets. An improvement of 7.6% and 7% was observed in the mIoU and BF score when the real time MR dataset of brain tumour was applied to the network. The network was incorporated using depthwise separable convolution.read more
Citations
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References
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Proceedings ArticleDOI
Deep Residual Learning for Image Recognition
TL;DR: In this article, the authors proposed a residual learning framework to ease the training of networks that are substantially deeper than those used previously, which won the 1st place on the ILSVRC 2015 classification task.
Proceedings Article
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan,Andrew Zisserman +1 more
TL;DR: This work investigates the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting using an architecture with very small convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers.
Book ChapterDOI
U-Net: Convolutional Networks for Biomedical Image Segmentation
TL;DR: Neber et al. as discussed by the authors proposed a network and training strategy that relies on the strong use of data augmentation to use the available annotated samples more efficiently, which can be trained end-to-end from very few images and outperforms the prior best method (a sliding-window convolutional network) on the ISBI challenge for segmentation of neuronal structures in electron microscopic stacks.
Proceedings ArticleDOI
Going deeper with convolutions
Christian Szegedy,Wei Liu,Yangqing Jia,Pierre Sermanet,Scott Reed,Dragomir Anguelov,Dumitru Erhan,Vincent Vanhoucke,Andrew Rabinovich +8 more
TL;DR: Inception as mentioned in this paper is a deep convolutional neural network architecture that achieves the new state of the art for classification and detection in the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC14).
Journal ArticleDOI
ImageNet classification with deep convolutional neural networks
TL;DR: A large, deep convolutional neural network was trained to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes and employed a recently developed regularization method called "dropout" that proved to be very effective.