A serial image analysis architecture with positron emission tomography using machine learning combined for the detection of lung cancer

Introduction and objectives

Lung cancer is the second type of cancer with the second highest incidence rate and the first with the highest mortality rate in the world. Machine learning through the analysis of imaging tests such as positron emission tomography/computed tomography (PET/CT) has become a fundamental tool for the early and accurate detection of cancer. The objective of this study was to propose an image analysis architecture (PET/CT) ordered in phases through the application of ensemble or combined machine learning methods for the early detection of lung cancer by analyzing PET/CT images.

Material and methods

A retrospective observational study was conducted utilizing a public dataset entitled “A large-scale CT and PET/CT dataset for lung cancer diagnosis.” Various imaging modalities, including CT, PET, and fused PET/CT images, were employed. The architecture or framework of this study comprised the following phases: 1. Image loading or collection, 2. Image selection, 3. Image transformation, and 4. Balancing the frequency distribution of image classes. Predictive models for lung cancer detection using PET/CT images included: a) the Stacking model, which used Random Forest and Support Vector Machine (SVM) as base models and complemented them with a logistic regression model, and b) the Boosting model, which employed the Adaptive Boosting (AdaBoost) model for comparison with the Stacking model. Quality metrics used for evaluation included accuracy, precision, recall, and F1-score.

Results

This study showed a general performance of 94% with the Stacking method and a general performance of 77% with the Boosting method.

Conclusions

The Stacking method proved to be a model with high performance and quality for lung cancer detection when analyzing PET/CT images.