Fusion of multimodal images for the detection of lung cancer lesions can synthesize different modal image features and break through the limitation of single modality. In this paper, 500 lung cancer patients in a hospital were selected as research objects, and ROI segmentation and image filtering were successively performed on their lesion image group data to complete the preprocessing of the image group data. Then Pyradiomics technique is used to extract image features with clear contour details from tumor regions in magnetic resonance (MRI) type images based on five filters. Subsequently, a hierarchical multimodal feature and classifier fusion framework is proposed based on the MCF algorithm. The filtered image features are input into the framework, and each modal feature is selected individually, and the model training and information fusion are carried out in a hierarchical manner to build a prediction model based on multimodal features and classifiers. Compared with similar modeling algorithms, the correlation of most of the features extracted from CT and PET images by the prediction model in this paper reaches 0.2 or above, which shows excellent performance of multimodal image feature screening for lung cancer.
