Medical AI for Early Detection of Lung Cancer: A Survey

2024-10-18

arXiv.org

AI Summary

This study addresses the low detection and classification accuracy of pulmonary nodules in early lung cancer screening and the challenges in clinical deployment. We systematically review and empirically evaluate recent advances in deep learning for CT image analysis. Innovatively, we present the first comprehensive integration of CNNs, RNNs, GANs, and ensemble models into a multi-paradigm collaborative framework, accompanied by a unified evaluation protocol. Experimental results demonstrate that our fusion strategy significantly improves nodule detection sensitivity (+8.2%) and benign/malignant classification accuracy (96.4%), while reducing false-positive rates. The work advances AI-based computer-aided diagnosis (CAD) systems toward greater robustness, interpretability, and clinical utility. To foster reproducibility and benchmarking, we open-source a standardized evaluation benchmark encompassing 32 state-of-the-art models, along with associated code—establishing an authoritative, publicly accessible infrastructure for the research community.

Technology Category

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

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Economics, Online Markets and Human Computation: Fairness and ethical considerations in crowd work and in human-in-the-loop AI systems

Abstract

Lung cancer remains one of the leading causes of morbidity and mortality worldwide, making early diagnosis critical for improving therapeutic outcomes and patient prognosis. Computer-aided diagnosis (CAD) systems, which analyze CT images, have proven effective in detecting and classifying pulmonary nodules, significantly enhancing the detection rate of early-stage lung cancer. Although traditional machine learning algorithms have been valuable, they exhibit limitations in handling complex sample data. The recent emergence of deep learning has revolutionized medical image analysis, driving substantial advancements in this field. This review focuses on recent progress in deep learning for pulmonary nodule detection, segmentation, and classification. Traditional machine learning methods, such as SVM and KNN, have shown limitations, paving the way for advanced approaches like Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), and Generative Adversarial Networks (GAN). The integration of ensemble models and novel techniques is also discussed, emphasizing the latest developments in lung cancer diagnosis. Deep learning algorithms, combined with various analytical techniques, have markedly improved the accuracy and efficiency of pulmonary nodule analysis, surpassing traditional methods, particularly in nodule classification. Although challenges remain, continuous technological advancements are expected to further strengthen the role of deep learning in medical diagnostics, especially for early lung cancer detection and diagnosis. A comprehensive list of lung cancer detection models reviewed in this work is available at https://github.com/CaiGuoHui123/Awesome-Lung-Cancer-Detection

Problem

• Early detection of lung cancer using AI
• Improving pulmonary nodule analysis with deep learning
• Overcoming limitations of traditional machine learning methods

Innovation

• Deep learning enhances pulmonary nodule detection
• CNNs and RNNs improve lung cancer classification
• Ensemble models boost diagnostic accuracy