Journal ArticleDOI
Improved Breast Cancer Classification Through Combining Graph Convolutional Network and Convolutional Neural Network
Yudong Zhang,Yudong Zhang,Suresh Chandra Satapathy,David S. Guttery,Juan Manuel Górriz,Shuihua Wang,Shuihua Wang +6 more
TLDR
The BDR-CNN-GCN showed improved performance compared to five proposed neural network models and 15 state-of-the-art breast cancer detection approaches, proving to be an effective method for data augmentation and improved detection of malignant breast masses.Abstract:
Aim In a pilot study to improve detection of malignant lesions in breast mammograms, we aimed to develop a new method called BDR-CNN-GCN, combining two advanced neural networks: (i) graph convolutional network (GCN); and (ii) convolutional neural network (CNN). Method We utilised a standard 8-layer CNN, then integrated two improvement techniques: (i) batch normalization (BN) and (ii) dropout (DO). Finally, we utilized rank-based stochastic pooling (RSP) to substitute the traditional max pooling. This resulted in BDR-CNN, which is a combination of CNN, BN, DO, and RSP. This BDR-CNN was hybridized with a two-layer GCN, and yielded our BDR-CNN-GCN model which was then utilized for analysis of breast mammograms as a 14-way data augmentation method. Results As proof of concept, we ran our BDR-CNN-GCN algorithm 10 times on the breast mini-MIAS dataset (containing 322 mammographic images), achieving a sensitivity of 96.20±2.90%, a specificity of 96.00±2.31% and an accuracy of 96.10±1.60%. Conclusion Our BDR-CNN-GCN showed improved performance compared to five proposed neural network models and 15 state-of-the-art breast cancer detection approaches, proving to be an effective method for data augmentation and improved detection of malignant breast masses.read more
Citations
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Journal ArticleDOI
Facial expression recognition via ResNet-50
Bin Li,Dimas Lima +1 more
TL;DR: This content proposes a method of feature extraction using the deep residual network ResNet-50, which combines convolutional neural network for facial emotion recognition and proves that this model is superior to the current mainstream facial emotion Recognition models in the performance of facial emotion detection.
Journal ArticleDOI
SARS-Net: COVID-19 Detection from Chest X-Rays by Combining Graph Convolutional Network and Convolutional Neural Network
TL;DR: SARS-Net as mentioned in this paper is a CADx system combining graph convolutional networks and Convolutional Neural Networks for detecting abnormalities in a patient's CXR images for presence of severe acute respiratory syndrome coronavirus.
Journal ArticleDOI
SARS-Net: COVID-19 detection from chest x-rays by combining graph convolutional network and convolutional neural network
TL;DR: SARS-Net as discussed by the authors is a CADx system combining graph convolutional networks and Convolutional Neural Networks for detecting abnormalities in a patient's CXR images for presence of severe acute respiratory syndrome coronavirus.
Journal ArticleDOI
Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Future
TL;DR: A survey of different types of graph architectures and their applications in healthcare can be found in this article, where the authors provide an overview of these methods in a systematic manner, organized by their domain of application including functional connectivity, anatomical structure and electrical-based analysis.
Journal ArticleDOI
Deep Learning based Assistive Technology on Audio Visual Speech Recognition for Hearing Impaired
TL;DR: In this article , a deep learning based audio visual speech recognition model for efficient lip reading was proposed, which achieved a lowered word error rate of about 6.59% for ASR system and accuracy of about 95% using lip reading model.
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Posted Content
Semi-Supervised Classification with Graph Convolutional Networks
Thomas Kipf,Max Welling +1 more
TL;DR: A scalable approach for semi-supervised learning on graph-structured data that is based on an efficient variant of convolutional neural networks which operate directly on graphs which outperforms related methods by a significant margin.
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Covid-19 Classification by FGCNet with Deep Feature Fusion from Graph Convolutional Network and Convolutional Neural Network.
Shuihua Wang,Shuihua Wang,Shuihua Wang,Vishnuvarthanan Govindaraj,Juan Manuel Górriz,Juan Manuel Górriz,Xin Zhang,Yudong Zhang,Yudong Zhang +8 more
TL;DR: The proposed FGCNet model can assist radiologists to rapidly detect COVID-19 from chest CT images and gives better performance than all 15 state-of-the-art methods.
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Deep learning radiomics can predict axillary lymph node status in early-stage breast cancer.
Xueyi Zheng,Zhao Yao,Yini Huang,Yanyan Yu,Yun Wang,Yubo Liu,Rushuang Mao,Fei Li,Yang Xiao,Yuanyuan Wang,Yixin Hu,Jinhua Yu,Jianhua Zhou +12 more
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