Cited by A novel lung cancer detection adopting Radiomic feature extraction with Locust assisted CS based CNN classifier

An Adaptive Dendritic Neural Model for Lung Cancer Prediction DOI

Umair Arif,

Chunxia Zhang,

Muhammad Waqas Chaudhary

et al.

Computer Methods in Biomechanics & Biomedical Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 1 - 14

Published: March 3, 2025

Lung cancer is a leading cause of cancer-related deaths, often diagnosed late due to its aggressive nature. This study presents novel Adaptive Dendritic Neural Model (ADNM) enhance diagnostic accuracy in high-dimensional healthcare data. Utilizing hyperparameter optimization and activation mechanisms, ADNM improves scalability feature selection for multi-class lung prediction. Using Kaggle dataset, Particle Swarm Optimization (PSO) selected features, while bootstrap assessed performance. achieved 98.39% accuracy, 99% AUC, Cohen's kappa 96.95%, with rapid convergence via the Adam optimizer, demonstrating potential improving early diagnosis personalized treatment oncology.

Language: Английский

Citations

Multigranularity feature aggregation in vision transformer for lung disease classification and segmentation DOI

Chao Xu,

Rong-Yao Ruan,

Yong Chen

et al.

Journal of Electronic Imaging, Journal Year: 2025, Volume and Issue: 34(02)

Published: April 25, 2025

Language: Английский

Citations

An Efficient Interpretable Stacking Ensemble Model for Lung Cancer Prognosis DOI

Umair Arif,

Chunxia Zhang,

Sajid Hussain

et al.

Computational Biology and Chemistry, Journal Year: 2024, Volume and Issue: 113, P. 108248 - 108248

Published: Oct. 16, 2024

Language: Английский

Citations

A novel lung cancer detection adopting Radiomic feature extraction with Locust assisted CS based CNN classifier DOI

P. Lavanya,

K. Vidhya

Biomedical Signal Processing and Control, Journal Year: 2024, Volume and Issue: 100, P. 107139 - 107139

Published: Nov. 6, 2024

Language: Английский

Citations