Enhancing COVID-19 Detection: An Xception-Based Model with Advanced Transfer Learning from X-ray Thorax Images
Rapid and precise identification of Coronavirus Disease 2019 (COVID-19) is pivotal for effective patient care, comprehending the pandemic’s trajectory, and enhancing long-term patient survival rates. Despite numerous recent endeavors in medical imaging, many convolutional neural network-based models...
Main Authors: | Reagan E. Mandiya, Hervé M. Kongo, Selain K. Kasereka, Kyamakya Kyandoghere, Petro Mushidi Tshakwanda, Nathanaël M. Kasoro |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2024-02-01
|
Series: | Journal of Imaging |
Subjects: | |
Online Access: | https://www.mdpi.com/2313-433X/10/3/63 |
Similar Items
-
Bone age recognition based on mask R-CNN using xception regression model
by: Zhi-Qiang Liu, et al.
Published: (2023-02-01) -
COVID-AleXception: A Deep Learning Model Based on a Deep Feature Concatenation Approach for the Detection of COVID-19 from Chest X-ray Images
by: Manel Ayadi, et al.
Published: (2022-10-01) -
A Modified Xception Deep Learning Model for Automatic Sorting of Olives Based on Ripening Stages
by: Seyed Iman Saedi, et al.
Published: (2023-12-01) -
High-Capacity Image Steganography Based on Improved Xception
by: Xintao Duan, et al.
Published: (2020-12-01) -
Covid-19 detection using modified xception transfer learning approach from computed tomography images
by: Kenan Morani, et al.
Published: (2023-11-01)