Covid19-Pneumonia-Normal Chest X-Ray Images

- It is a medical images directory structure branched into 3 subfolders (COVID, NORMAL, PNEUMONIA) containing Chest X-ray (CXR) Images. - All images are preprocessed and resized to 256x256 in PNG format. - It helps the researcher and medical community to detect and classify COVID19 and Pneumonia from Chest X-Ray Images using Deep Learning. COVID-19: 1626 images NORMAL: 1802 images PNEUMONIA: 1800 images References: -If you are using this dataset for research purposes then cite the below articles: 1. Shastri, S., Kansal, I., Kumar, S. et al. CheXImageNet: a novel architecture for accurate classification of Covid-19 with chest x-ray digital images using deep convolutional neural networks. Health Technol. 12, 193–204 (2022). https://doi.org/10.1007/s12553-021-00630-x 2. Kumar S, Shastri S, Mahajan S, et al. LiteCovidNet: A lightweight deep neural network model for detection of COVID-19 using X-ray images. Int J Imaging Syst Technol. 2022;1‐17. DOI: https://doi.org/10.1002/ima.22770

本数据集为医学图像目录结构，分为3个子文件夹（COVID、NORMAL、PNEUMONIA），内部存储胸部X射线（Chest X-ray, CXR）图像。
所有图像均已完成预处理，并统一调整至256×256分辨率，格式为PNG。
本数据集旨在助力研究人员与医疗群体借助深度学习技术，从胸部X射线图像中检测并分类新型冠状病毒肺炎（COVID-19）与肺炎（PNEUMONIA）。
各类别样本量如下：COVID-19类包含1626张图像，NORMAL类包含1802张图像，PNEUMONIA类包含1800张图像。
参考文献：
1. Shastri S, Kansal I, Kumar S 等. CheXImageNet：一种基于深度卷积神经网络的新型架构，可通过胸部X射线数字图像精准分类新型冠状病毒肺炎. Health Technol. 12, 193–204 (2022). https://doi.org/10.1007/s12553-021-00630-x
2. Kumar S, Shastri S, Mahajan S 等. LiteCovidNet：一种基于X射线图像检测新型冠状病毒肺炎的轻量级深度学习神经网络模型. Int J Imaging Syst Technol. 2022;1‐17. DOI: https://doi.org/10.1002/ima.22770

搜集汇总

背景与挑战

背景概述

该数据集包含三类胸部X光图像（COVID、NORMAL、PNEUMONIA），共计5228张预处理后的256x256像素PNG图像，主要用于COVID-19和肺炎的深度学习检测与分类研究。