Deep Learning-Based Imbalanced Data Classification for Chest X-Ray Image Analysis

2021 ◽  
pp. 109-115
Author(s):  
Dang Xuan Tho ◽  
Dao Nam Anh
Keyword(s):  
Image Analysis ◽  
Deep Learning ◽  
Imbalanced Data ◽  
Data Classification ◽  
X Ray ◽  
Imbalanced Data Classification ◽  
Chest X Ray

Detection of peripherally inserted central catheter (PICC) in chest X-ray images: A multi-task deep learning model

2020 ◽  
Vol 197 ◽  
pp. 105674
Author(s):  
Dingding Yu ◽  
Kaijie Zhang ◽  
Lingyan Huang ◽  
Bonan Zhao ◽  
Xiaoshan Zhang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Model ◽  
Peripherally Inserted Central Catheter ◽  
Central Catheter ◽  
X Ray ◽  
Chest X Ray ◽  
Deep Learning Model

scholarly journals Pneumonia Classification Using Deep Learning from Chest X-ray Images During COVID-19

2021 ◽  
Author(s):  
Abdullahi Umar Ibrahim ◽  
Mehmet Ozsoz ◽  
Sertan Serte ◽  
Fadi Al-Turjman ◽  
Polycarp Shizawaliyi Yakoi
Keyword(s):  
Deep Learning ◽  
X Ray ◽  
Chest X Ray

scholarly journals Deep learning and evolutionary intelligence with fusion-based feature extraction for detection of COVID-19 from chest X-ray images

2021 ◽  
Author(s):  
K. Shankar ◽  
Eswaran Perumal ◽  
Prayag Tiwari ◽  
Mohammad Shorfuzzaman ◽  
Deepak Gupta
Keyword(s):  
Feature Extraction ◽  
Deep Learning ◽  
X Ray ◽  
Chest X Ray ◽  
Evolutionary Intelligence

scholarly journals Accessory pathway analysis using a multimodal deep learning model

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Makoto Nishimori ◽  
Kunihiko Kiuchi ◽  
Kunihiro Nishimura ◽  
Kengo Kusano ◽  
Akihiro Yoshida ◽  
...  
Keyword(s):  
Deep Learning ◽  
Electrophysiological Study ◽  
Accessory Pathway ◽  
Learning Model ◽  
X Rays ◽  
X Ray ◽  
Wpw Syndrome ◽  
Chest X Ray ◽  
Deep Learning Model ◽  
Ecg Data

AbstractCardiac accessory pathways (APs) in Wolff–Parkinson–White (WPW) syndrome are conventionally diagnosed with decision tree algorithms; however, there are problems with clinical usage. We assessed the efficacy of the artificial intelligence model using electrocardiography (ECG) and chest X-rays to identify the location of APs. We retrospectively used ECG and chest X-rays to analyse 206 patients with WPW syndrome. Each AP location was defined by an electrophysiological study and divided into four classifications. We developed a deep learning model to classify AP locations and compared the accuracy with that of conventional algorithms. Moreover, 1519 chest X-ray samples from other datasets were used for prior learning, and the combined chest X-ray image and ECG data were put into the previous model to evaluate whether the accuracy improved. The convolutional neural network (CNN) model using ECG data was significantly more accurate than the conventional tree algorithm. In the multimodal model, which implemented input from the combined ECG and chest X-ray data, the accuracy was significantly improved. Deep learning with a combination of ECG and chest X-ray data could effectively identify the AP location, which may be a novel deep learning model for a multimodal model.

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