Research on Cancer Prediction Based on Feature Optimization and Multimodal Fusion.

ABSTRACT:

Background

Current lung cancer initial diagnosis relies on experienced doctors combining imaging and biological indicators, but uneven medical resource distribution in China leads to delayed early diagnosis, affecting prognosis. Existing methods struggle with large-scale screening, multitracking, and over-reliance on single-modality data, ignoring the potential of multisource complementary information. Key technical challenges-effective data collection, multimodal feature extraction/fusion, and AI model construction-limit clinical application. Thus, exploring AI, new sensors, and existing data for efficient, fast, accurate, and radiation-free preliminary diagnosis is crucial for timely treatment and improved outcomes.

Methods

This study collected hematological data, and used

SUBMITTER: Xu J 

PROVIDER: S-EPMC12728679 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature