Breast cancer cell pathological slides for deep learning model training and analysis of breast cancer risk factors.
Description
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
Files
Steps to reproduce
The dataset used in this study is sourced from the publicly available medical imaging database of AiStudio. The original images exhibited significant resolution differences, so image normalization was performed to obtain a final set of 250 high-quality images. Image labeling was performed using labelimg software. Due to the small size of the dataset, and to mitigate the potential impact of limited data on model training, the validation set was merged with the training set. The 250 images were then divided into training and testing sets in an 80:20 ratio. We collected data on breast cancer risk factors and patient information from the UCI website and analyzed the risk factors. The data on breast cancer risk factors were cleaned and organized using SPSS software, with some sexually transmitted diseases with low incidence rates deleted, cases with missing data excluded, and the most significant risk factors selected as the dataset for model construction and validation.
Institutions
- Guangdong Polytechnic Normal University