A deep learning mobile-based image analysis for cervical cancer detection

This dataset provides a structured environment for cervical cancer image analysis using machine learning and deep learning. It includes four main experiments: Risk factor prediction using classical ML models ROI segmentation using U-Net Lesion detection using Detectron2 Binary classification of images and Kappa evaluation The experiments are implemented in Jupyter notebooks. All datasets are reduced 10% samples. The images used are publicly available from the Intel MobileODT dataset. Originally, the project included a private dataset (CAIME), but for privacy reasons, those images were removed and replaced with public samples. Both Intel/ and test/ folders now contain only public data. Segmentation masks (.tif) were also included where filenames matched. This environment was originally executed in a Docker container with GPU support (NVIDIA QUADRO), but the reduced version can be tested on CPU.本数据集为宫颈癌影像分析的机器学习与深度学习研究提供了结构化实验环境，包含四项核心实验： 1. 基于经典机器学习模型的风险因子预测 2. 基于U-Net的感兴趣区域（ROI）分割 3. 基于Detectron2的病灶检测 4. 影像二分类与Kappa评估 所有实验均通过Jupyter Notebook实现。所有数据集均缩减了10%的样本量，所用影像均公开获取自Intel MobileODT数据集。本项目最初包含私有数据集CAIME，但出于隐私保护考量，相关影像已被移除并替换为公开样本。当前的Intel/与test/文件夹仅包含公开数据，当文件名匹配时，亦提供了对应的分割掩码（.tif格式）。 该实验环境最初基于支持GPU加速的Docker容器（搭载NVIDIA QUADRO显卡）搭建，但缩减版数据集可在CPU环境下进行测试。