Application of Three-dimensional Coding Network in Screening and Diagnosis of Cervical Precancerous Lesions

Abstract

Globally, the incidence of cervical cancer ranks fourth among female malignant tumors, seriously threatening the physical and mental health of women. Early detection and early treatment can greatly reduce the mortality of cervical cancer "cytology /HPV test, colposcopy and cervical biopsy" is the main method for clinical diagnosis of cervical cancer. The progress of medical technology has significantly improved the early diagnosis of cervical cancer, but due to various factors, there are still many cases of missed diagnosis and misdiagnosis. In recent years, artificial intelligence has developed rapidly in the medical field, and has also shown good applicability in the screening and diagnosis of cervical cancer. In this paper, we design an mcs-SEM structure that contains both channels and space compression excitation modules, which can retain comprehensive spatial information at a low computational cost. Then, the structure is embedded in a three-dimensional coding network to realize the combination of two-dimensional convolutional neural network and three-dimensional spatial information, so as to predict the DVH in the complex distribution scenario of multi-endangered organs with higher accuracy.