Medical image classification by incorporating clinical variables and learned features
Medical image classification plays an important role in medical imaging. In this work, we present a novel approach to enhance deep learning models in medical image classification by incorporating clinical variables without overwhelming the information. Unlike most existing deep neural network models...
Saved in:
| Main Authors: | Jiahui Liu, Xiaohao Cai, Mahesan Niranjan |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
The Royal Society
2025-03-01
|
| Series: | Royal Society Open Science |
| Subjects: | |
| Online Access: | https://royalsocietypublishing.org/doi/10.1098/rsos.241222 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Application of Quantitative Interpretability to Evaluate CNN-Based Models for Medical Image Classification
by: Nuan Cui, et al.
Published: (2025-01-01) -
Improved Quaternion Discriminant Analysis for Feature Extraction and Classification of Hyperspectral Image
by: Xinpeng Wang, et al.
Published: (2025-01-01) -
RobustDeiT: Noise-Robust Vision Transformers for Medical Image Classification
by: Mehdi Taassori
Published: (2025-06-01) -
Texture Aware Deep Feature Map Based Linear Weighted Medical Image Fusion
by: Vijayarajan Rajangam, et al.
Published: (2022-01-01) -
Computational Brain Imaging Framework for Neurological Mapping and Disorder Classification Using Multimodal Image Processing
by: S. Karthikeyan, et al.
Published: (2025-05-01)