Artificial Intelligence Model-Assisted Accurate Diagnosis of Early-Stage Breast Cancer

For the collected patient data, deep learning is used to perform feature screening on the selected or collected images, and malignant risk factors are determined by combining clinical significance. A neural network classifier is trained on the training set data. Variable selection: independent variables (breast MRI images, breast ultrasound images, indicators such as CA199, CA153, CA125, AFP/CEA, etc.), dependent variables (whether suffering from breast cancer and breast cancer subtypes), and the verification accuracy standard is set as the pathological biopsy result.

The accuracy of a breast cancer prediction model is typically evaluated using multiple metrics that assess its performance in different aspects