scholarly journals COVID‐19 Diagnosis on CT Scan Images Using a Generative Adversarial Network and Concatenated Feature Pyramid Network with an Attention Mechanism

2021 ◽  
Author(s):  
Zonggui Li ◽  
Junhua Zhang ◽  
Bo Li ◽  
Xiaoying Gu ◽  
Xudong Luo
Keyword(s):  
Ct Scan ◽  
Attention Mechanism ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Feature Pyramid

scholarly journals CT-scan Denoising using a Charbonnier Loss Generative Adversarial Network

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Binit Gajera ◽  
Siddhant Kapil ◽  
Dorsa Ziaei ◽  
Jayalakshmi Mangalagiri ◽  
Eliot Siegel ◽  
...  
Keyword(s):  
Ct Scan ◽  
Generative Adversarial Network ◽  
Adversarial Network

scholarly journals HeartNet: Self Multi-Head Attention Mechanism via Convolutional Network with Adversarial Data Synthesis for ECG-based Arrhythmia Classification

2021 ◽  
Author(s):  
Taki Hasan Rafi ◽  
Young Woong-Ko
Keyword(s):  
Deep Learning ◽  
Attention Mechanism ◽  
Classification Systems ◽  
Insufficient Data ◽  
Learning Method ◽  
Generative Adversarial Network ◽  
Convolutional Network ◽  
Data Synthesis ◽  
Adversarial Network ◽  
Main Challenge

Cardiovascular disease is now one of the leading causes of morbidity and mortality in humans. Electrocardiogram (ECG) is a reliable tool for monitoring the health of the cardiovascular system. Currently, there has been a lot of focus on accurately categorizing heartbeats. There is a high demand on automatic ECG classification systems to assist medical professionals. In this paper we proposed a new deep learning method called HeartNet for developing an automatic ECG classifier. The proposed deep learning method is compressed by multi-head attention mechanism on top of CNN model. The main challenge of insufficient data label is solved by adversarial data synthesis adopting generative adversarial network (GAN) with generating additional training samples. It drastically improves the overall performance of the proposed method by 5-10% on each insufficient data label category. We evaluated our proposed method utilizing MIT-BIH dataset. Our proposed method has shown 99.67 ± 0.11 accuracy and 89.24 ± 1.71 MCC trained with adversarial data synthesized dataset. However, we have also utilized two individual datasets such as Atrial Fibrillation Detection Database and PTB Diagnostic Database to see the performance of our proposed model on ECG classification. The effectiveness and robustness of proposed method are validated by extensive experiments, comparison and analysis. Later on, we also highlighted some limitations of this work.

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