Selection of HBV key reactivation factors based on maximum information coefficient combined with cosine similarity.

Background: Hepatitis B Virus (HBV) reactivation is the most common complication for patients with primary liver cancer (PLC) after radiotherapy. How to reduce the reactivation of HBV has been a hot topic in the study of postoperative radiotherapy for liver cancer.

Objective: To find out the inducement of HBV reactivation, a feature selection algorithm (MIC-CS) using maximum information coefficient (MIC) combined with cosine similarity (CS) was proposed to screen the risk factors that may affect HBV reactivation.

Image classification of vaginal microecology detection based on gabor texture and LSTM model.

Background: Gynecological diseases threaten women's health, and vaginal microecological testing is a common method for detecting gynecological diseases. Efficient and accurate microecological testing methods have always been the goal pursued by gynecologists.

Objective: In order to automatically identify different types of microbial images in vaginal micromorphology detection, this paper proposes a vaginal microecological image recognition method based on Gabor texture analysis combined with long and short-term memory network (LSTM) model.

Differentiating Grade in Breast Invasive Ductal Carcinoma Using Texture Analysis of MRI.

Purpose: The objective of this study is to investigate the use of texture analysis (TA) of magnetic resonance image (MRI) enhanced scan and machine learning methods for distinguishing different grades in breast invasive ductal carcinoma (IDC). Preoperative prediction of the grade of IDC can provide reference for different clinical treatments, so it has important practice values in clinic.

Methods: Firstly, a breast cancer segmentation model based on discrete wavelet transform (DWT) and -means algorithm is proposed.