Análisis de Factores de Riesgo Basado en Aprendizaje Profundo para la Predicción Precisa del Cáncer de Pulmón en...

Lung cancer remains a leading cause of cancer-related deaths worldwide, largely due to late diagnosis and the complexity of its risk factors. Early detection and accurate risk prediction are critical to improving patient survival and reducing treatment costs.

This study presents a novel deep learning framework combining advanced techniques such as the Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI), Whale Optimization Algorithm with Adaptive Particle Swarm Optimization (WOA-APSO), convolutional neural networks (CNN), and Kernel-based non-Gaussian CNN (KNG-CNN) implemented in PYTHON to enhance lung cancer risk prediction.

The proposed model effectively optimizes feature selection and achieves a high prediction accuracy of 99.25%. These findings demonstrate the potential of integrating deep learning and optimization algorithms for precise risk stratification, facilitating early diagnosis, and personalized treatment.

This work underscores the transformative impact of AI-driven approaches in lung cancer prognosis and highlights future opportunities for improving clinical outcomes.