Enhancing Medical X-Ray Image Classification with Neutrosophic Set Theory and Advanced Deep Learning Models
The classification of medical images presents significant challenges due to the presence of noise, uncertainty, and indeterminate information. Traditional deep learning models often struggle to manage this, leading to reduced diagnostic accuracy, especially when dealing with low-quality or ambiguous...
Saved in:
| Main Author: | Walid Abdullah |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
University of New Mexico
2025-04-01
|
| Series: | Neutrosophic Sets and Systems |
| Subjects: | |
| Online Access: | https://fs.unm.edu/NSS/41ImageClassification.pdf |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Neutrosophic Topological Spaces for Lung Cancer Detection in Chest X-Rays: A Novel Approach to Uncertainty Management
by: A. A. Salama, et al.
Published: (2025-03-01) -
RMPT: Reinforced Memory-Driven Pure Transformer for Automatic Chest X-Ray Report Generation
by: Caijie Qin, et al.
Published: (2025-04-01) -
An Approach for Breast Cancer X-Ray Images Classification Based on Vision Transformer
by: Huong Hoang Luong, et al.
Published: (2025-08-01) -
Modified AlexNet Convolution Neural Network For Covid-19 Detection Using Chest X-ray Images
by: Shadman Q. Salih, et al.
Published: (2020-06-01) -
Realistic wave-optics simulation of X-ray dark-field imaging at a human scale
by: Yongjin Sung, et al.
Published: (2025-07-01)