数据集获取

git clone https://github.com/rgbnihal2/COVID-19-X-ray-Dataset.git

curl -L -o repo.zip https://github.com/rgbnihal2/COVID-19-X-ray-Dataset/archive/refs/heads/master.zip
unzip repo.zip

源站 README 摘录（使用方式）

COVID-19-X-ray-Dataset

Full Paper available at: https://www.preprints.org/manuscript/202008.0215/v2
Background/introduction: The ravage of COVID-19 is not merely limited to taking its toll with half a million fatalities. It has halted the world economy, disrupting normalcy of lives with supervening severity than any other global catastrophe ofthe last few decades. The majority of the vaccine discovery attempts are still ontrial, making early detection and containment the only feasible redress. The existing diagnostic technique with high accuracy has the setbacks of being expensive and sophisticated, requiring skilled individuals for specimen collection and screening resulting in lower outreach. So, methods excluding direct human inter-vention are much sought after, and artificial intelligence-driven automated diag-nosis, especially with radiography images, captures the researchers’ interest.
Method: This survey marks a detailed inspection of the deep-learning-based au-tomated detection of COVID-19 works done to date, methodical challenges along with probable solutions, and scopes of future exploration in this arena. We also provided a comparative quantitative analysis of the performance of 315 deep models in diagnosing COVID-19, Normal, and Pneumonia from x-ray images of a custom dataset created from four others. The dataset is publicly available at https://github.com/rgbnihal2/COVID-19-X-ray-Dataset.
Result:Our results show that DenseNet201 model with Quadratic SVM classifier performs the best (accuracy: 98.16%, sensitivity: 98.93%, specificity: 98.77%) and maintains high accuracies in other similar architectures as well. This proves thateven though radiography images might not be conclusive for radiologists, but it is so for deep learning algorithms for detecting COVID-19.
Conclusion:We hope this extensive review will provide a comprehensive guidelinefor researchers in this field.
The sources of the dataset are:
1.https://github.com/ieee8023/covid-chestxray-dataset
2.https://github.com/agchung/Figure1-COVID-chestxray-dataset
3.https://github.com/agchung/Actualmed-COVID-chestxray-dataset
4.https://www.kaggle.com/tawsifurrahman/covid19-radiography-database
Cite our paper with :

Rahman, S.; Sarker, S.; Al Miraj, A.; Amin Nihal, R.; Haque, A.K.M.N.; Al Noman, A. Deep Learning Driven Automated Detection of COVID-19 from Radiography Images: A Comparative Anal

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/rgbnihal2/COVID-19-X-ray-Dataset