Journal ArticleDOI
RLDS: An explainable residual learning diagnosis system for fetal congenital heart disease
TLDR
Wang et al. as discussed by the authors proposed a simple yet effective residual learning diagnosis system (RLDS) for diagnosing fetal CHD to improve diagnostic accuracy, which adopts convolutional neural networks to extract discriminative features of the fetal cardiac anatomical structures.About:
This article is published in Future Generation Computer Systems.The article was published on 2022-03-01. It has received 20 citations till now. The article focuses on the topics: Fetus & Feature (linguistics).read more
Citations
More filters
Journal ArticleDOI
A Review on Deep-Learning Algorithms for Fetal Ultrasound-Image Analysis
Maria Chiara Fiorentino,Francesca Pia Villani,Mariachiara Di Cosmo,Emanuele Frontoni,Sara Moccia +4 more
TL;DR: A detailed survey of the most recent work in the field can be found in this paper , with a total of 145 research papers published after 2017 and each paper is analyzed and commented on from both the methodology and application perspective.
Guided Random Forests for identification of key fetal anatomy and image categorization in ultrasound scans
TL;DR: A novel machine learning based method to categorize unlabeled fetal ultrasound images that utilizes a translation and orientation invariant feature which captures the appearance of a region at multiple spatial resolutions is proposed.
Journal ArticleDOI
The severity prediction of the binary and multi-class cardiovascular disease − A machine learning-based fusion approach
TL;DR: In this article , a weighted score fusion approach was taken to improve the performance of classification, and two algorithms' decision was combined using a weighted sum rule, and the proposed approach has been experimented with different test training ratios for binary and multiclass classification problems.
Journal ArticleDOI
The Severity Prediction of The Binary And Multi-Class Cardiovascular Disease - A Machine Learning-Based Fusion Approach
Hafsa Binte Kibria,Abdul Matin +1 more
TL;DR: In this research, some fusion models have been constructed to diagnose CVDs along with its severity, and results were promising in the performance parameter.
Journal ArticleDOI
TVS: a trusted verification scheme for office documents based on blockchain
TL;DR: Li et al. as discussed by the authors proposed a trusted verification scheme (TVS) for office documents to ensure security, which is based on the features of blockchain, such as security, credibility, immutability, and traceability of network behavior.
References
More filters
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.
Proceedings ArticleDOI
ImageNet: A large-scale hierarchical image database
TL;DR: A new database called “ImageNet” is introduced, a large-scale ontology of images built upon the backbone of the WordNet structure, much larger in scale and diversity and much more accurate than the current image datasets.
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.
Related Papers (5)
Fetal Heart Baseline Extraction And Classification based on Deep Learning
Weisheng Gao,Yao Lu +1 more
Fetal Cardiac Function: Technical Considerations and Potential Research and Clinical Applications
Fatima Crispi,Eduard Gratacós +1 more