Lung cancer detection from thoracic CT scans using an ensemble of deep learning models

Lung cancer remains a prevalent and deadly disease, claiming numerous lives annually. Early detection plays a pivotal role in significantly improving survival rates, by up to 50–70%. Therefore, developing a robust lung cancer detection system holds immense potential to positively impact human survival. Computed tomography (CT) scan images offer invaluable information about lung nodules, and the emergence of machine learning and deep learning techniques has empowered radiologists in their diagnostic tasks. In this study, we propose a new ensemble of deep learning models to accurately classify the severity of lung nodules. Our approach leverages deep transfer learning and adopts an ensemble learning approach. Specifically, three state-of-the-art convolutional neural networks (CNN) models, namely ResNet-152, DenseNet-169, and EfficientNet-B7, are employed. To enhance the ensemble method’s performance, we introduce a novel scheme for selecting and assigning weights to each base model. Unlike conventional methods that often rely on manual experimentation to set weights, our approach fuses the scores of two standard assessment metrics, ROC-AUC score and F1-score, for a more accurate weight vector determination. To evaluate the effectiveness of our method, we conduct extensive testing using the publicly available CT scan dataset, LIDC-IDRI. Our proposed ensemble achieves an accuracy of 97.23%, surpassing various recent methods and outperforming commonly used ensemble techniques. Furthermore, our novel weight optimization strategy significantly reduces false negatives, leading to a sensitivity of 98.6%. The codes for the proposed work are available at https://github.com/iabh1shekbasu/LungCancerDetectionEnsemble.