CerviFusionNet: A multi-modal, hybrid CNN-transformer-GRU model for enhanced cervical lesion multi-classification.

ABSTRACT: Cervical lesions pose a significant threat to women's health worldwide. Colposcopy is essential for screening and treating cervical lesions, but its effectiveness depends on the doctor's experience. Artificial intelligence-based solutions via colposcopy images have shown great potential in cervical lesions screening. However, some challenges still need to be addressed, such as low algorithm performance and lack of high-quality multi-modal datasets. Here, we established a multi-modal colposcopy dataset of 2,273 HPV+ patients, comprising original colposcopy images, acetic acid reactions at 60s and 120s, iodine staining, diagnostic reports, and pathological results. Utilizing this dataset, we developed CerviFusionNet, a hybrid architecture that merges convolutional neural networks and vision

SUBMITTER: Sha Y 

PROVIDER: S-EPMC11615576 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature