IMPROVING PREDICTION ACCURACY OF DEEP LEARNING FOR BRAIN CANCER DIAGNOSIS USING POLYAK-RUPPERT OPTIMIZATION
Accurate and reliable diagnosis is critical for effective treatment planning for brain cancer. Recent advancements in deep learning have significantly enhanced diagnostic capabilities, but challenges persist in optimizing model performance for diverse and complex datasets. This study investigates th...
Saved in:
Main Authors: | M Muthulekshmi, Azath Mubarakali, Y M Blessy |
---|---|
Format: | Article |
Language: | English |
Published: |
XLESCIENCE
2024-12-01
|
Series: | International Journal of Advances in Signal and Image Sciences |
Subjects: | |
Online Access: | https://xlescience.org/index.php/IJASIS/article/view/173 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Short Paper - Quadratic minimization: from conjugate gradient to an adaptive Polyak’s momentum method with Polyak step-sizes
by: Goujaud, Baptiste, et al.
Published: (2024-11-01) -
Alzheimer’s disease diagnosis by 3D-SEConvNeXt
by: Zhongyi Hu, et al.
Published: (2025-01-01) -
GastroFuse-Net: an ensemble deep learning framework designed for gastrointestinal abnormality detection in endoscopic images
by: Sonam Aggarwal, et al.
Published: (2024-08-01) -
A Synergy of Convolutional Neural Networks for Sensor-Based EEG Brain–Computer Interfaces to Enhance Motor Imagery Classification
by: Souheyl Mallat, et al.
Published: (2025-01-01) -
Diagnosis of oral cancer using deep learning algorithms
by: Mayra Alejandra Dávila Olivos, et al.
Published: (2024-10-01)