Bin Hu, Haitao Zhou, Yue Wang, Li Zheng, Min Li
A study of satisfaction with research capacity development of Master of Public Health and its influencing factors
Frontiers in public health
cervical cancer major health concern worldwide highlighting urgent need better early detection methods improve outcomes patients study present novel digital pathology classification approach combines lowrank adaptation lora vision transformer vit model method aimed making cervix type classification efficient deep learning classifier require much data key innovation use lora allows effective training model smaller datasets making ability vit represent visual information approach performs better traditional convolutional neural network cnn models including residual networks resnets especially comes performance ability generalize situations data limited thorough experiments analysis various dataset sizes found streamlined classifier highly accurate spotting various cervical anomalies across several cases work advances development sophisticated computeraided diagnostic systems facilitating rapid accurate detection cervical cancer thereby significantly enhancing patient care outcomes