Home

Awesome

Medical AI for Early Detection of Lung Cancer: A Survey

Authors: Guohui Cai, Ying Cai*, Zeyu Zhang, Yuanzhouhan Cao, Lin Wu, Daji Ergu, Zhinbin Liao, Yang Zhao

*Corresponding author

[Paper Link] [Papers With Code]

img

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.

Project Overview

This project focuses on the application of deep learning techniques to the detection, segmentation, and classification of pulmonary nodules in CT images, particularly for early-stage lung cancer detection. The methods leverage advanced neural networks such as Convolutional Neural Networks (CNNs), U-Nets, and their variants to improve diagnostic accuracy, reduce false positives, and enhance the overall sensitivity of Computer-Aided Diagnosis (CAD) systems.

The project is built upon two prominent datasets: LIDC-IDRI and LUNA16, both of which are publicly available and widely used in lung nodule research. By utilizing these datasets, the project aims to achieve a more comprehensive analysis of the performance of deep learning models in the medical imaging field.

Datasets

The project utilizes several key datasets that have been essential in driving advancements in lung nodule detection and diagnosis:

Key Techniques and Models

This project explores multiple deep learning models tailored to different aspects of lung nodule detection, segmentation, and classification:

Detection Models

Segmentation Models

Classification Models

Performance Metrics

The performance of the models is evaluated using the following key metrics:

Future Work

This project highlights the potential of deep learning models in improving the accuracy of lung nodule detection and classification. However, future work will focus on:

Contributors

Acknowledgements

This research has been supported by the National Natural Science Foundation of China (Grant No. 72174172) and the Scientific and Technological Innovation Team for Qinghai-Tibetan Plateau Research at Southwest Minzu University (Grant No. 2024CXTD20). We sincerely appreciate their valuable support, which made this work possible.

Key Papers