Journal ArticleDOI
Automatic segmentation of brain tumour in MR images using an enhanced deep learning approach
TLDR
A fully automatic deep learning method to quantify the tumour region in brain Magnetic Resonance images as the accurate diagnosis of brain tumours region is necessary for the treatment of the patients is proposed.Abstract:
The presented manuscript proposes a fully automatic deep learning method to quantify the tumour region in brain Magnetic Resonance images as the accurate diagnosis of brain tumour region is necessa...read more
Citations
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Journal ArticleDOI
A Systematic Approach for MRI Brain Tumor Localization and Segmentation Using Deep Learning and Active Contouring.
TL;DR: In this paper, a threefold deep learning architecture is proposed for tumor extraction and segmentation of tumor boundaries correctly, which includes a deep convolutional neural network (CNN), a region-based CNN and a Chan-Vese segmentation algorithm.
Journal ArticleDOI
Four Types of Multiclass Frameworks for Pneumonia Classification and Its Validation in X-ray Scans Using Seven Types of Deep Learning Artificial Intelligence Models
Nillmani,Pankaj Jain,Neeraj Sharma,Mannudeep K. Kalra,Klaudija Višković,Luca Saba,Jasjit S. Suri +6 more
TL;DR: A deep learning AI-based automatic multiclass detection and classification of pneumonia from chest X-ray images that are readily available and highly cost-effective, and outperformed existing methods by 1.2% for the five-class model.
Journal ArticleDOI
Encoder Modified U-Net and Feature Pyramid Network for Multi-class Segmentation of Cardiac Magnetic Resonance Images
TL;DR: Cardiovascular diseases are leading cause of death worldwide and timely and accurate detection of disease is required to reduce load on healthcare system and number of deaths.
Journal ArticleDOI
An Augmented Deep Learning Network with Noise Suppression Feature for Efficient Segmentation of Magnetic Resonance Images
TL;DR: The segmentation of cardiac MR images requires extensive attention as it needs a high level of care and analysis for the diagnosis of affected part.
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
ImageNet Classification with Deep Convolutional Neural Networks
TL;DR: The state-of-the-art performance of CNNs was achieved by Deep Convolutional Neural Networks (DCNNs) as discussed by the authors, which consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax.
Proceedings Article
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan,Andrew Zisserman +1 more
TL;DR: In this paper, the authors investigated the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting and showed that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 layers.
Journal ArticleDOI
Gradient-based learning applied to document recognition
Yann LeCun,Léon Bottou,Léon Bottou,Yoshua Bengio,Yoshua Bengio,Yoshua Bengio,Patrick Haffner +6 more
TL;DR: In this article, a graph transformer network (GTN) is proposed for handwritten character recognition, which can be used to synthesize a complex decision surface that can classify high-dimensional patterns, such as handwritten characters.
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).