Breast cancer cell pathological slides for deep learning model training and analysis of breast cancer risk factors.

The dataset is created by using Convolutional Neural Networks (CNN) to automatically extract cancer cell features from breast cancer tissue pathology images. The extracted features are then used for model training, and the final model can accurately identify and locate cancer cells in breast cancer tissue pathology images.This dataset divides 250 breast cancer cell pathology slides into a training set and a testing set in an 8:2 ratio. The locations of the cancer cells are marked with cell bounding boxes, and the model is tested using the testing set. The final result includes some labeled images generated from the test set.
we employ the random forest model to analyze patient information, including age, lifestyle habits, environmental factors, and other triggers. Various factors are scored, and effective cancer prevention strategies are developed based on the influence of these triggers on the disease