Self-Supervised Deep Convolutional Neural Network for Chest X-Ray Classification
Chest radiography is a relatively cheap, widely available medical procedure that conveys key information for making diagnostic decisions. Chest X-rays are frequently used in the diagnosis of respiratory diseases such as pneumonia or COVID-19. In this paper, we propose a self-supervised deep neural n...
Saved in:
Main Authors: | Matej Gazda, Jan Plavka, Jakub Gazda, Peter Drotar |
---|---|
Format: | article |
Language: | EN |
Published: |
IEEE
2021
|
Subjects: | |
Online Access: | https://doaj.org/article/55228cfe99e54f56b099ecb78b7fa61b |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
A Multi-Stage GAN for Multi-Organ Chest X-ray Image Generation and Segmentation
by: Giorgio Ciano, et al.
Published: (2021) -
Pneumonia detection in chest X-ray images using compound scaled deep learning model
by: Mohammad Farukh Hashmi, et al.
Published: (2021) -
An Interaction-Based Convolutional Neural Network (ICNN) Toward a Better Understanding of COVID-19 X-ray Images
by: Shaw-Hwa Lo, et al.
Published: (2021) -
A Deep-Learning Approach to Detect Fiducials in Planar X-Ray Images for Undistortion of Conventional C-Arm Images
by: Alvarez-Gomez Julio, et al.
Published: (2020) -
Review of Image Classification Algorithms Based on Convolutional Neural Networks
by: Leiyu Chen, et al.
Published: (2021)