LungCT-NET: An explainable transfer learning-based robust ensemble model for lung cancer diagnosis

Abstract

Lung cancer, one of the most prevalent and deadliest diseases, necessitates early detection for patient survival. The low level of contrast between lesions and adjacent lung tissue, coupled with the diverse shapes and structures of lung nodules, poses significant challenges for their accurate identification and classification. Despite the extensive use of machine learning methods, the lack of adequately annotated datasets significantly hampers efficient model training. Moreover, the lack of transparency in deep learning models has led to their perception as “black boxes”, constraining their credibility for end users like radiologists. To address these issues, we present LungCT-NET , a novel transfer learning-based architecture coupled with ensemble learning and explainable AI for binary classification of lung nodules into malignant and benign using lung CT scans. LungCT-NET incorporates essential preprocessing, reconfiguring transfer learning models, and an advanced stacking ensemble strategy utilizing combinations of the top-performing pre-trained models. Several transfer learning algorithms are employed, including VGG-16, VGG-19, MobileNet-V2, InceptionNet-V3, EfficientNet-B0, ResNet152-V2, and DenseNet-121. Extensive experimental analyses have been carried out on the LIDC-DIRI dataset using various performance metrics for evaluation. The findings demonstrate that the suggested framework significantly exceeds state-of-the-art approaches, achieving an accuracy, precision, F1 score and recall of 98.99%, an AUC of 98.15%. Finally, the integrated SHapley Additive exPlanations (SHAP) enhance the grasp of model outcomes, hence increasing confidence in lung cancer prognosis. Therefore, the proposed innovative LungCT-NET can potentially support clinical settings by automating lung nodule classification from low-dose CT scans, aiding physicians and radiologists in prompt, accurate diagnoses.

Topics & Concepts