ENSEMBLE-X: Interpretable Dual-Pipeline Model for CT-Based and Clinical Feature-Based Lung Cancer Detection

Authors

  • Nukala Sujana ,Dr. SK. Sajeeda Praveen Author

DOI:

https://doi.org/10.48047/ner7ey58

Keywords:

Lung Cancer Detection, CT Scan Analysis, Hybrid Diagnostic Model, Clinical Decision Support, Risk Prediction.

Abstract

Early detection of lung cancer is pivotal to reducing mortality rates and improving clinical outcomes. This paper introduces a 
hybrid computational framework that synergistically combines classical machine learning algorithms with convolutional neural 
networks (CNNs) to enhance diagnostic accuracy for lung cancer detection. 

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Published

25.04.2025

Issue

Vol. 15 No. 4 (2025): volume 15 issue 4(2025)

Section

Articles

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How to Cite

ENSEMBLE-X: Interpretable Dual-Pipeline Model for CT-Based and Clinical Feature-Based Lung Cancer Detection . (2025). International Journal of Information and Electronics Engineering, 15(4), 636-642. https://doi.org/10.48047/ner7ey58