Deep Learning Network Selection and Optimized Information Fusion for Enhanced COVID-19 Detection: A Literature Review
The rapid spread of COVID-19 increased the need for speedy diagnostic tools, which led scientists to conduct extensive research on deep learning (DL) applications that use chest imaging, such as chest X-ray (CXR) and computed tomography (CT). This review examines the development and performance of D...
Saved in:
Main Authors: | Olga Adriana Caliman Sturdza, Florin Filip, Monica Terteliu Baitan, Mihai Dimian |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2025-07-01
|
Series: | Diagnostics |
Subjects: | |
Online Access: | https://www.mdpi.com/2075-4418/15/14/1830 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Optimization of deep neural network for multiclassification of Pneumonia
by: G Divya Deepak
Published: (2024-12-01) -
A Hybrid Deep Transfer Learning-based Approach for COVID-19 Classification in Chest X-ray Images
by: Khosro Rezaee, et al.
Published: (2021-12-01) -
Enhancing Multi-Label Chest X-Ray Classification Using an Improved Ranking Loss
by: Muhammad Shehzad Hanif, et al.
Published: (2025-05-01) -
The Invisible Threat That Leaves You Breathless—A Literature Review on Pneumothorax in the Emergency Department
by: Silvia Fattori, et al.
Published: (2025-05-01) -
HyCoViT: Hybrid Convolution Vision Transformer With Dynamic Dropout for Enhanced Medical Chest X-Ray Classification
by: Omid Almasi Naghash, et al.
Published: (2025-01-01)