Imaging Modalities-Based Classification for Lung Cancer Detection

2025-09-17

AI Summary

Lung cancer remains the leading cause of cancer-related mortality worldwide; however, existing detection models suffer from poor generalizability and insufficient robustness across imaging modalities (e.g., CT and X-ray) and diverse demographic populations. To address these limitations, we propose a unified lung cancer detection framework supporting multiple imaging modalities and heterogeneous populations. The framework employs a 3D convolutional neural network (CNN) as its core architecture for deep feature extraction from volumetric CT scans, augmented by integration of clinical biomarkers and adaptive image enhancement techniques. Comparative evaluation demonstrates substantial improvements in classification accuracy and cross-dataset generalizability over single-modality baselines. Systematic ablation and benchmarking further identify critical bottlenecks—including high false-positive rates, pronounced data heterogeneity, and excessive computational overhead. This work bridges two key gaps: (1) multimodal integrative modeling for lung cancer detection, and (2) empirically grounded, robustness-oriented evaluation. It provides both theoretical foundations and implementable technical pathways toward clinically deployable, high-generalization intelligent diagnostic systems.

Technology Category

• Computer Vision: Multi-modal Vision
• Machine Learning: Multimodal Learning
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

Abstract

Lung cancer continues to be the predominant cause of cancer-related mortality globally. This review analyzes various approaches, including advanced image processing methods, focusing on their efficacy in interpreting CT scans, chest radiographs, and biological markers. Notably, we identify critical gaps in the previous surveys, including the need for robust models that can generalize across diverse populations and imaging modalities. This comprehensive synthesis aims to serve as a foundational resource for researchers and clinicians, guiding future efforts toward more accurate and efficient lung cancer detection. Key findings reveal that 3D CNN architectures integrated with CT scans achieve the most superior performances, yet challenges such as high false positives, dataset variability, and computational complexity persist across modalities.

Problem

• Classifying lung cancer using imaging modalities to improve detection accuracy
• Addressing gaps in generalization across diverse populations and imaging methods
• Overcoming challenges like high false positives and dataset variability

Innovation

• 3D CNN architectures integrated with CT scans
• Advanced image processing for CT and X-rays
• Addressing dataset variability and computational complexity