乳腺癌威斯康星诊断数据集
该数据集包含从乳腺肿块细针穿刺细胞核图像中提取的30个数值特征，数据模态为临床表格数据，主要用于乳腺癌的良恶性二分类研究，是机器学习在医学影像分析和辅助诊断领域的经典基准数据集，广泛应用于分类算法（如支持向量机、决策树）的性能评估和模型开发。
基本信息
资源简介
乳腺癌威斯康星诊断数据集
【数据集背景】
本数据集源站标题为《AkshayMewada/ML-on-Breast-Cancer-Wisconsin-Diagnostic-Dataset-using-Scikit-Learn》。源站首次发布：2017-10-13。
【数据内容】
源站原始描述：# ML-on-Breast-Cancer-Wisconsin-Diagnostic-Dataset-using-Scikit-Learn #Dataset: Download Dataset Dataset have Attribute Information: 1) ID number 2) Diagnosis (M = malignant, B = benign) 3-32) Ten real-valued features are computed for each cell nucleus: a) radius (mean of distances from center to points on the perimeter) b) texture (standard deviation of gray-scale values) c) perimeter d) area e) smoothness (local variation in radius lengths) f) compactness (perimeter^2 / area - 1.0) g) concavit…
【获取与许可】
源站页面：https://github.com/AkshayMewada/ML-on-Breast-Cancer-Wisconsin-Diagnostic-Dataset-using-Scikit-Learn
下载信息
注册下载
需要注册 Kaggle 账号并登录后下载，适合需要跟踪下载记录和使用 API 的用户。
暂未开放公开下载
无需注册即可直接获取公开样本或文档，适合快速预览和评估数据集质量。
免登录有偿下载
公开数据集受托下载与技术交付服务。
提供高速下载与技术交付服务（收技术服务费，非数据销售）
暂未开放当前数据集主要来源为 Kaggle 公开托管，完整影像包建议通过原始链接或 Kaggle API 获取。
使用方式
数据集获取
git clone https://github.com/AkshayMewada/ML-on-Breast-Cancer-Wisconsin-Diagnostic-Dataset-using-Scikit-Learn.git
curl -L -o repo.zip https://github.com/AkshayMewada/ML-on-Breast-Cancer-Wisconsin-Diagnostic-Dataset-using-Scikit-Learn/archive/refs/heads/master.zip
unzip repo.zip
源站 README 摘录（使用方式）
ML-on-Breast-Cancer-Wisconsin-Diagnostic-Dataset-using-Scikit-Learn
#Dataset:
Download Dataset https://www.kaggle.com/uciml/breast-cancer-wisconsin-data
Dataset have Attribute Information:
- ID number 2) Diagnosis (M = malignant, B = benign) 3-32)
Ten real-valued features are computed for each cell nucleus:
a) radius (mean of distances from center to points on the perimeter) b) texture (standard deviation of gray-scale values) c) perimeter d) area e) smoothness (local variation in radius lengths) f) compactness (perimeter^2 / area - 1.0) g) concavity (severity of concave portions of the contour) h) concave points (number of concave portions of the contour) i) symmetry j) fractal dimension (“coastline approximation” - 1)
The mean, standard error and “worst” or largest (mean of the three largest values) of these features were computed for each image, resulting in 30 features. For instance, field 3 is Mean Radius, field 13 is Radius SE, field 23 is Worst Radius.
Module used
- Numpy
- Pandas
- matplotlib
-seaborn
-Scikit-Learn
Used models
- Gaussian-Naive-Byes
- K-Nearest-Neighbors
- Support-Vector-Machine
- Logistic-Regression
- Decision-Tree
- Random-Forest
数据加载示例（图像类）
from PIL import Image
import glob, os
files = (glob.glob(os.path.join(path, "**", "*.png"), recursive=True)
+ glob.glob(os.path.join(path, "**", "*.jpg"), recursive=True)
+ glob.glob(os.path.join(path, "**", "*.tif"), recursive=True))
print("图像文件数:", len(files))
img = Image.open(files[0]); print("尺寸/模式:", img.size, img.mode)
# torchvision Dataset 方式:
# from torchvision import datasets
# ds = datasets.ImageFolder(path) # 要求 子目录=类别
目录组织与标注格式以源站说明和下载后实际文件为准。
完整仓库：github.com/AkshayMewada/ML-on-Breast-Cancer-Wisconsin-Diagnostic-Dataset-using-Scikit-Learn
数据缺失？
依托陕西、四川两大基地，我们与超过十家三甲医院建立直接合作关系，覆盖合规授权、采集治理、专业标注、数据交付的全流程，为AI医疗团队提供即拿即用的高质量临床数据。
⚡️ 需要数据支持或标注服务？立即联系我们获取专业方案。
