Background and objective: Current researches on pulmonary nodules mainly focused on the binary-classification of benign and malignant pulmonary nodules. However, in clinical applications, it is not enough to judge whether pulmonary nodules are benign or malignant. In this paper, we proposed a fusion model based on the Lung Information Dataset Containing 3D CT Images and Serum Biomarkers (LIDCCISB) we constructed to accurately diagnose the types of pulmonary nodules in squamous cell carcinoma, adenocarcinoma, inflammation and other benign diseases.
Methods: Using single modal information of lung 3D CT images and single modal information of Lung Tumor Biomarkers (LTBs) in LIDCCISB, a Multi-resolution 3D Multi-classification deep learning model (Mr-Mc) and a Multi-Layer Perceptron machine learning model (MLP) were constructed for diagnosing multiple pathological types of pulmonary nodules, respectively. To comprehensively use the double modal information of CT images and LTBs, we used transfer learning to fuse Mr-Mc and MLP, and constructed a multimodal information fusion model that could classify multiple pathological types of benign and malignant pulmonary nodules.
Results: Experiments showed that the constructed Mr-Mc model can achieve an average accuracy of 0.805 and MLP model can achieve an average accuracy of 0.887. The fusion model was verified on a dataset containing 64 samples, and achieved an average accuracy of 0.906.
Conclusions: This is the first study to simultaneously use CT images and LTBs to diagnose multiple pathological types of benign and malignant pulmonary nodules, and experiments showed that our research was more advanced and more suitable for practical clinical applications.
Keywords: Machine learning; Multi-resolution deep learning; Multimodal information fusion; Multiple pathological types; Pulmonary nodules.
Copyright © 2021. Published by Elsevier B.V.