Lung Cancer Detection - Dataset.zip

This dataset comprises medical images categorized into four distinct classes: Adenocarcinoma, Large Cell Carcinoma, Squamous Cell Carcinoma, and Normal. The dataset includes a total of 1,000 images, with 338 images labeled as Adenocarcinoma, 187 as Large Cell Carcinoma, 260 as Squamous Cell Carcinoma, and 215 as Normal. The images are primarily in PNG format (988 images) with a small fraction in JPG format (12 images). The average image dimensions are 258 pixels in height and 356 pixels in width.The dataset is structured into three subsets: training, validation, and test sets, ensuring proper evaluation and model generalization. Additionally, a separate category, referred to as "bad images," stores non-readable or corrupted images that are unsuitable for processing. The dataset provides a valuable resource for developing and evaluating deep learning models for lung cancer detection and classification.

本数据集包含按四类明确类别划分的医学图像：腺癌（Adenocarcinoma）、大细胞癌（Large Cell Carcinoma）、鳞状细胞癌（Squamous Cell Carcinoma）以及正常组织样本。数据集总计包含1000张图像，其中腺癌样本338张、大细胞癌样本187张、鳞状细胞癌样本260张、正常组织样本215张。图像主要采用PNG格式（共988张），仅少量为JPG格式（共12张）。图像平均尺寸为高度258像素、宽度356像素。本数据集划分为训练集、验证集与测试集三个子集，以确保可对模型开展合理评估并保障其泛化能力。此外，数据集还设有单独的“不良图像”类别，用于存储无法读取或已损坏、不适用于模型处理的图像。本数据集可为肺癌检测与分类任务的深度学习模型开发及评估提供宝贵的研究资源。