Machine Learning Model of ResNet50-Ensemble Voting for Malignant–Benign Small Pulmonary Nodule Classification on Computed Tomography Images

SIMPLE SUMMARY: Machine learning methods have shown promise in accurately identifying small lung nodules. However, further exploration is needed to fully harness the potential of machine learning in distinguishing between benign and malignant nodules. This study aimed to develop and evaluate a ResNet50-Ensemble Voting model for detecting the nature (benign or malignant) of small pulmonary nodules (less than 20 mm) based on CT images. This study involved 834 CT imaging data from 396 patients with small pulmonary nodules. CT image features were extracted using ResNet50 and VGG16 algorithms, and classification was performed using XGBoost, SVM, and Ensemble Voting techniques, incorporating ten different combinations of machine learning classifiers. Among the models tested, the ResNet50-Ensemble Voting algorithm demonstrated the highest performance in the test set, achieving an accuracy of 0.943 (0.938, 0.948), with sensitivity and specificity values of 0.964 and 0.911, respectively. The implementation of machine learning models, particularly the ResNet50-Ensemble Voting approach, showed excellent performance in accurately identifying benign and malignant small pulmonary nodules (less than 20 mm) from diverse sources. These models have the potential to assist doctors in accurately diagnosing the nature of early-stage lung nodules in clinical practice. ABSTRACT: Background: The early detection of benign and malignant lung tumors enabled patients to diagnose lesions and implement appropriate health measures earlier, dramatically improving lung cancer patients’ quality of living. Machine learning methods performed admirably when recognizing small benign and malignant lung nodules. However, exploration and investigation are required to fully leverage the potential of machine learning in distinguishing between benign and malignant small lung nodules. Objective: The aim of this study was to develop and evaluate the ResNet50-Ensemble Voting model for detecting the benign and malignant nature of small pulmonary nodules (<20 mm) based on CT images. Methods: In this study, 834 CT imaging data from 396 patients with small pulmonary nodules were gathered and randomly assigned to the training and validation sets in an 8:2 ratio. ResNet50 and VGG16 algorithms were utilized to extract CT image features, followed by XGBoost, SVM, and Ensemble Voting techniques for classification, for a total of ten different classes of machine learning combinatorial classifiers. Indicators such as accuracy, sensitivity, and specificity were used to assess the models. The collected features are also shown to investigate the contrasts between them. Results: The algorithm we presented, ResNet50-Ensemble Voting, performed best in the test set, with an accuracy of 0.943 (0.938, 0.948) and sensitivity and specificity of 0.964 and 0.911, respectively. VGG16-Ensemble Voting had an accuracy of 0.887 (0.880, 0.894), with a sensitivity and specificity of 0.952 and 0.784, respectively. Conclusion: Machine learning models that were implemented and integrated ResNet50-Ensemble Voting performed exceptionally well in identifying benign and malignant small pulmonary nodules (<20 mm) from various sites, which might help doctors in accurately diagnosing the nature of early-stage lung nodules in clinical practice.