COVID-19 chest radiography employing via Deep Convolutional Stacked Autoencoders

Zhi Hao Chen, Jyh Ching Juang

研究成果: Conference contribution

摘要

This paper applies AI (artificial intelligence) technology to analyze CT (chest radiography) data in an attempt to detect COVID-19 pneumonia symptoms. A new model structure is proposed with segmentation of anatomical structures on DNNs-based (deep learning neural network) methods, relying on an abundance of labeled data for proper training. The model improves the existing techniques used for CT images inspection through an application of Stacked Autoencoders (SAEs) structures using the segmentation function for the area object detection model on Fast-RCNN. As a result, the proposed approach can quickly analyze X-ray images in detecting abnormalities in patients with lab-confirmed coronavirus even before clinical symptoms appear. In addition to detecting early abnormalities, area object detection model reveals a finding not seen in the latest cases of COVID-19. Most noteworthy, the study has shown that all COVID-19 patients exhibit an associated bilateral pleural effusion. The features are augmented to the model for the improvement of detection quality improvement and the shorten of the examination period.

原文English
主出版物標題2020 International Automatic Control Conference, CACS 2020
發行者Institute of Electrical and Electronics Engineers Inc.
ISBN(電子)9781728171982
DOIs
出版狀態Published - 2020 十一月 4
事件2020 International Automatic Control Conference, CACS 2020 - Hsinchu, Taiwan
持續時間: 2020 十一月 42020 十一月 7

出版系列

名字2020 International Automatic Control Conference, CACS 2020

Conference

Conference2020 International Automatic Control Conference, CACS 2020
國家Taiwan
城市Hsinchu
期間20-11-0420-11-07

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Automotive Engineering
  • Industrial and Manufacturing Engineering
  • Control and Systems Engineering
  • Control and Optimization
  • Modelling and Simulation

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