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LUNGAI: 肺癌检测模型

项目概述

LungAI 是一个深度学习项目，旨在从 CT 扫描图像中检测和分类肺癌。该模型可以区分癌性和非癌性肺组织，并对特定类型的肺癌进行分类。

黑客马拉松 4 次获奖者 - 从 1,500 名参赛者中脱颖而出。

GitHub HuggingFace

模型性能

  • 98% 的准确率区分癌性和非癌性病例
  • 83% 的准确率区分四种特定类型的肺部疾病：
    • 腺癌：82% F1 分数
    • 大细胞癌：85% F1 分数
    • 正常（非癌性）：98% F1 分数
    • 鳞状细胞癌：76% F1 分数

<i>本项目代表最新版本，现已使用 PyTorch。</i>

仓库结构

  • Architecture/：包含核心模型脚本
    • architecture.py：定义模型架构
    • preprocess.py：数据预处理工具
    • test.py：模型测试脚本
  • Model/：存储训练好的模型文件
    • lung_cancer_detection_model.onnx：训练模型的 ONNX 格式
    • lung_cancer_detection_model.pth：训练模型的 PyTorch 权重
  • Data/：（不包含在仓库中）存储数据集的目录
  • Processed_Data/：（不包含在仓库中）预处理数据的目录
  • assets/：其他项目资源
  • requirements.txt：Python 依赖项列表

设置与使用

步骤 1：安装依赖

首先，确保已安装 Python。然后，使用以下命令安装所需的 Python 库：

pip install -r requirements.txt

步骤 2：训练模型（可选）

运行训练脚本来训练模型。 它将保存为 .pth 和 .onnx 文件

python Architecture/architecture.py

步骤 3：运行模型

运行以下文件来使用模型：

python Architecture/run.py

注意事项

  • 确保您的数据集在 Processed_Data 目录下正确构建，并包含训练、验证和测试集的子目录。
  • 模型训练脚本要求数据集位于 Processed_Data 目录中。请确保在 architecture.py 中正确设置了数据转换和目录路径。

贡献

如果您想为本项目做出贡献，请 fork 本仓库并提交 pull request。我们欢迎改进、错误修复和新功能。

联系我

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dorsar/lung-cancer-detection

作者 dorsar

↓ 0 ♥ 3

创建时间: 2024-06-30 14:40:49+00:00

更新时间: 2024-08-13 22:17:50+00:00

在 Hugging Face 上查看

文件 (776)

.gitattributes
Data/test/adenocarcinoma/000108 (3).png
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Data/valid/squamous.cell.carcinoma_left.hilum_T1_N2_M0_IIIa/000108 (3).png
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Model/lung_cancer_detection_model.onnx ONNX
Model/lung_cancer_detection_model.pth
README.md
app.py
architecture.py
preprocess.py
requirements.txt
run.py