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Lung cancer is the leading cause of cancer death worldwide, highlighting the urgent need for faster, more accurate methods for early prediction. With decades of experience at the intersection of AI and oncology, I will guide you through the latest AI strategies designed to meet this challenge, focusing on a new multimodal architecture: HeteroFusion-LungNet. We will assess how current models blend clinical histories with CT scans and tackle persistent issues like model interpretability, architectural resilience, and precise feature engineering. HeteroFusion-LungNet builds upon existing research by employing an attention-infused ensemble to seamlessly integrate structured clinical data with CT visuals. The model follows a methodical pipeline, beginning with Dynamic Noise-Structure Alignment (DNSA) for data preprocessing.Next, an advanced MAFT++ model, augmented with Cross-Modal Attention Filters (CMAF), selects the most salient features. For the core analysis, Multi-Level Capsule Attention Networks (ML-CapANet) and Feature Correlation Encoders (FCE) work in tandem to uncover critical patterns. The process culminates in an Ensemble of Cross-Space Experts (ECSE), which synthesizes outputs from the various deep learning modules to generate a single, robust prediction.This architecture not only underscores the power of multimodal integration but also serves as a practical blueprint for the next generation of clinical tools. By prioritizing accuracy, interpretability, and reliability, HeteroFusion-LungNet represents a critical effort to bridge the gap between advanced research and daily medical practice.

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Multimodal Machine and Deep Learning Approaches for Lung Cancer Prediction: A Survey and the HeteroFusion-LungNet Model Using CT and Clinical Data

  • 会议论文

  • 发表日期：

    2026年

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  • N Viswanadha Reddy,
  • Bobba Veeramallu

摘要

Lung cancer is the leading cause of cancer death worldwide, highlighting the urgent need for faster, more accurate methods for early prediction. With decades of experience at the intersection of AI and oncology, I will guide you through the latest AI strategies designed to meet this challenge, focusing on a new multimodal architecture: HeteroFusion-LungNet. We will assess how current models blend clinical histories with CT scans and tackle persistent issues like model interpretability, architectural resilience, and precise feature engineering. HeteroFusion-LungNet builds upon existing research by employing an attention-infused ensemble to seamlessly integrate structured clinical data with CT visuals. The model follows a methodical pipeline, beginning with Dynamic Noise-Structure Alignment (DNSA) for data preprocessing.Next, an advanced MAFT++ model, augmented with Cross-Modal Attention Filters (CMAF), selects the most salient features. For the core analysis, Multi-Level Capsule Attention Networks (ML-CapANet) and Feature Correlation Encoders (FCE) work in tandem to uncover critical patterns. The process culminates in an Ensemble of Cross-Space Experts (ECSE), which synthesizes outputs from the various deep learning modules to generate a single, robust prediction.This architecture not only underscores the power of multimodal integration but also serves as a practical blueprint for the next generation of clinical tools. By prioritizing accuracy, interpretability, and reliability, HeteroFusion-LungNet represents a critical effort to bridge the gap between advanced research and daily medical practice.