Proceedings ArticleDOI
Capsule Networks for Brain Tumor Classification Based on MRI Images and Coarse Tumor Boundaries
Parnian Afshar,Konstantinos N. Plataniotis,Arash Mohammadi +2 more
- pp 1368-1372
Reads0
Chats0
TLDR
In this article, a modified CapsNet architecture is proposed for brain tumor classification, which takes the tumor coarse boundaries as extra inputs within its pipeline to increase the CapsNet's focus.Abstract:
According to official statistics, cancer is considered as the second leading cause of human fatalities. Among different types of cancer, brain tumor is seen as one of the deadliest forms due to its aggressive nature, heterogeneous characteristics, and low relative survival rate. Determining the type of brain tumor has significant impact on the treatment choice and patient’s survival. Human-centered diagnosis is typically error-prone and unreliable resulting in a recent surge of interest to automatize this process using convolutional neural networks (CNNs). CNNs, however, fail to fully utilize spatial relations, which is particularly harmful for tumor classification, as the relation between the tumor and its surrounding tissue is a critical indicator of the tumor’s type. In our recent work, we have incorporated newly developed CapsNets to overcome this shortcoming. CapsNets are, however, highly sensitive to the miscellaneous image background. The paper addresses this gap. The main contribution is to equip CapsNet with access to the tumor surrounding tissues, without distracting it from the main target. A modified CapsNet architecture is, therefore, proposed for brain tumor classification, which takes the tumor coarse boundaries as extra inputs within its pipeline to increase the CapsNet’s focus. The proposed approach noticeably outperforms its counterparts.read more
Citations
More filters
Journal ArticleDOI
Brain tumor classification using deep CNN features via transfer learning.
S. Deepak,P. M. Ameer +1 more
TL;DR: A 3-class classification problem to differentiate among glioma, meningioma and pituitary tumors, which form three prominent types of brain tumor is focused on, which adopts the concept of deep transfer learning and uses a pre-trained GoogLeNet to extract features from brain MRI images.
Journal ArticleDOI
COVID-CAPS: A Capsule Network-based Framework for Identification of COVID-19 cases from X-ray Images.
Parnian Afshar,Shahin Heidarian,Farnoosh Naderkhani,Anastasia Oikonomou,Konstantinos N. Plataniotis,Arash Mohammadi +5 more
TL;DR: Results based on a dataset of X-ray images show that COVID-CAPS has advantage over previous CNN-based models, being capable of handling small datasets, which is of significant importance due to sudden and rapid emergence of CO VID-19.
Journal ArticleDOI
Deep convolutional neural network based medical image classification for disease diagnosis
TL;DR: This paper researches how to apply the convolutional neural network (CNN) based algorithm on a chest X-ray dataset to classify pneumonia and shows that data augmentation generally is an effective way for all three algorithms to improve performance.
Journal ArticleDOI
Deep neural network with generative adversarial networks pre-training for brain tumor classification based on MR images
TL;DR: A deep neural network is first pre-trained as a discriminator in a generative adversarial network on different datasets of MR images to extract robust features and to learn the structure of MR pictures in its convolutional layers.
Journal ArticleDOI
Deep Learning for Multigrade Brain Tumor Classification in Smart Healthcare Systems: A Prospective Survey
TL;DR: This survey covers the main steps of deep learning-based BTC methods, including preprocessing, features extraction, and classification, along with their achievements and limitations, and investigates the state-of-the-art convolutional neural network models for BTC by performing extensive experiments using transfer learning with and without data augmentation.
References
More filters
Proceedings Article
Adam: A Method for Stochastic Optimization
Diederik P. Kingma,Jimmy Ba +1 more
TL;DR: This work introduces Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments, and provides a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework.
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.
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.
Journal ArticleDOI
Cancer statistics, 2016
TL;DR: Overall cancer incidence trends are stable in women, but declining by 3.1% per year in men, much of which is because of recent rapid declines in prostate cancer diagnoses, and brain cancer has surpassed leukemia as the leading cause of cancer death among children and adolescents.
Proceedings Article
Dynamic Routing Between Capsules
TL;DR: It is shown that a discrimininatively trained, multi-layer capsule system achieves state-of-the-art performance on MNIST and is considerably better than a convolutional net at recognizing highly overlapping digits.
Related Papers (5)
Brain tumor classification using deep CNN features via transfer learning.
S. Deepak,P. M. Ameer +1 more