Multi-Layer Perceptron Classifier with the Proposed Combined Feature Vector of 3D CNN Features and Lung Radiomics Features for COPD Stage Classification

Abstract

Computed tomography (CT) has been regarded as the most effective modality for characterizing and quantifying chronic obstructive pulmonary disease (COPD). Therefore, chest CT images should provide more information for COPD diagnosis, such as COPD stage classification. This paper proposes a features combination strategy by concatenating three-dimension (3D) CNN features and lung radiomics features for COPD stage classification based on the multi-layer perceptron (MLP) classifier. First, 465 sets of chest HRCT images are automatically segmented by a trained ResU-Net, obtaining the lung images with the Hounsfield unit. Second, the 3D CNN features are extracted from the lung region images based on a truncated transfer learning strategy. Then, the lung radiomics features are extracted from the lung region images by PyRadiomics. Third, the MLP classifier with the best classification performance is determined by the 3D CNN features and the lung radiomics features. Finally, the proposed combined feature vector is used to improve the MLP classifier’s performance. The results show that compared with CNN models and other ML classifiers, the MLP classifier with the best classification performance is determined. The MLP classifier with the proposed combined feature vector has achieved accuracy, mean precision, mean recall, mean F1-score, and AUC of 0.879, 0.879, 0.879, 0.875, and 0.971, respectively. Compared to the MLP classifier with the 3D CNN features selected by Lasso, our method based on the MLP classifier has improved the classification performance by 5.8% (accuracy), 5.3% (mean precision), 5.8% (mean recall), 5.4% (mean F1-score), and 2.5% (AUC). Compared to the MLP classifier with lung radiomics features selected by Lasso, our method based on the MLP classifier has improved the classification performance by 5.0% (accuracy), 5.1% (mean precision), 5.0% (mean recall), 5.1% (mean F1-score), and 2.1% (AUC). Therefore, it is concluded that our method is effective in improving the classification performance for COPD stage classification.

A Multichannel CT and Radiomics-Guided CNN-ViT (RadCT-CNNViT) Ensemble Network for Diagnosis of Pulmonary Sarcoidosis

“…Although there have been previous attempts to combine CNN and ViT in various forms for disease diagnosis, we believe this is one of the first use cases of using radiomics texture maps and CT as 3D volumetric, multichannel inputs in a CNN-ViT framework. Previous studies have typically shown a combination of radiomics and CNN features for lung disease classification, prognosis and staging using late fusion techniques, i.e., radiomics features and CNN features were combined just before the classification layer, which demonstrated improved performance compared to CNN features or radiomics features based classification only [39,40,62]. In our previous work [55], we showed how radiomics texture features used as input to a CNN-ViT framework had improved performance over using radiomics features with a traditional machine learning classifier to classify pulmonary sarcoidosis from other ILDs.…”

“…For example, radiomic features were extracted from CT image and used in a deep learning network, which is an example of early fusion, and then further combined with clinical features at a late stage for the prediction of the EGFR gene mutation status for non-small cell lung carcinoma [39]. Similarly, radiomics features were extracted separately and then combined with features derived from CNN and fused at an intermediate stage before the classification of COPD staging [40] and lung nodule classification [28,41]. All methods that extracted radiomic features, however, depended on defining a region of interest, except in Liang et al [42], where radiomics features were extracted from the entire lung, although the lung parenchyma was segmented.…”