Dataset Summary

The dataset is organized into 3 folders (train, test, val) and contains subfolders for each image category (Pneumonia/Normal). There are 5,863 X-Ray images (JPEG) and 2 categories (Pneumonia/Normal).

Chest X-ray images (anterior-posterior) were selected from retrospective cohorts of pediatric patients of one to five years old from Guangzhou Women and Children’s Medical Center, Guangzhou. All chest X-ray imaging was performed as part of patients’ routine clinical care.

For… See the full description on the dataset page: https://huggingface.co/datasets/hf-vision/chest-xray-pneumonia.

Chest X-ray images for pneumonia detection.

Dataset of publicly available images from COVID-19 positive patients collected from several sources over the net. All images are chest x-rays from frontal view (AP or PA). There is a ZIP file containing 900 images and a metadata in CSV format which includes information about 452 images.Note that some of the images are from pediatrics and/or from early-stage patients with no specific image findings noted by the radiologist; but all of them are from COVID-positive cases. Related guideline and details are available in the GitHub repo.

Context

http://www.cell.com/cell/fulltext/S0092-8674(18)30154-5

https://i.imgur.com/jZqpV51.png" alt="">

Figure S6. Illustrative Examples of Chest X-Rays in Patients with Pneumonia, Related to Figure 6 The normal chest X-ray (left panel) depicts clear lungs without any areas of abnormal opacification in the image. Bacterial pneumonia (middle) typically exhibits a focal lobar consolidation, in this case in the right upper lobe (white arrows), whereas viral pneumonia (right) manifests with a more diffuse ‘‘interstitial’’ pattern in both lungs. http://www.cell.com/cell/fulltext/S0092-8674(18)30154-5

Content

The dataset is organized into 3 folders (train, test, val) and contains subfolders for each image category (Pneumonia/Normal). There are 5,863 X-Ray images (JPEG) and 2 categories (Pneumonia/Normal).

Chest X-ray images (anterior-posterior) were selected from retrospective cohorts of pediatric patients of one to five years old from Guangzhou Women and Children’s Medical Center, Guangzhou. All chest X-ray imaging was performed as part of patients’ routine clinical care.

For the analysis of chest x-ray images, all chest radiographs were initially screened for quality control by removing all low quality or unreadable scans. The diagnoses for the images were then graded by two expert physicians before being cleared for training the AI system. In order to account for any grading errors, the evaluation set was also checked by a third expert.

Acknowledgements

Data: https://data.mendeley.com/datasets/rscbjbr9sj/2

License: CC BY 4.0

Citation: http://www.cell.com/cell/fulltext/S0092-8674(18)30154-5

https://i.imgur.com/8AUJkin.png" alt="enter image description here">

Inspiration

Automated methods to detect and classify human diseases from medical images.

This COVID-19 dataset consists of Non-COVID and COVID cases of both X-ray and CT images. The associated dataset is augmented with different augmentation techniques to generate about 17099 X-ray and CT images. The dataset contains two main folders, one for the X-ray images, which includes two separate sub-folders of 5500 Non-COVID images and 4044 COVID images. The other folder contains the CT images. It includes two separate sub-folders of 2628 Non-COVID images and 5427 COVID images.

Pneumonia Chest X Ray

## Overview

Pneumonia Chest X Ray is a dataset for object detection tasks - it contains Pneumonia Nopneumonia annotations for 200 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Description:

👉 Download the dataset here

This dataset provides a comprehensive collection of chest X-ray images representing three types of pneumonia: COVID-19 pneumonia, viral pneumonia, and bacterial pneumonia. The dataset is curate from 15 publicly available sources and has been meticulously process to ensure high-quality, relevant data for research and development in medical imaging, AI, and machine learning applications.

The dataset comprises the following categories of X-ray images:

COVID-19 Pneumonia: 1281 X-rays

Normal (No Pneumonia): 3270 X-rays

Viral Pneumonia: 1656 X-rays

Bacterial Pneumonia: 3001 X-rays

Download Dataset

Dataset Curation Process

The initial dataset, comprising over 19,000 images, was refine using image similarity algorithms to remove duplicates, noisy images, and other defects. The Inception V3 model was employe to extract image embeddings, which were further analyze using unsupervise learning techniques to filter out images that exhibite poor quality or anomalies. Images exhibiting defects such as noise, pixelation, compression artifacts, and medical implants were systematically remove to ensure the dataset’s integrity.

Features of the Dataset

Diverse Representation: The dataset provides X-rays for three distinct types of pneumonia, offering an ideal foundation for training AI models in medical diagnostics.

Cleaned and Curated: All duplicate and faulty images have been removed, with the final dataset being subjected to quality control processes such as image clustering and manual review.

Visualization and Disease Highlighting: Tools such as Inception V3 have been utilize to visually highlight abnormalities and disease characteristics, making the dataset highly suitable for

visualization-base medical research.

Common Image Defects Addressed

Throughout the dataset cleaning process, several types of image defects were identify and addressed. These include:

Noise and Pixelation: Images with significant noise and pixelation were remove to enhance clarity.

Compression Artifacts: X-rays affected by excessive compression were exclude.

Medical Implants: X-rays with visible implants that might interfere with pneumonia diagnosis were filtered out.

Washed-out Images: Images with poor contrast or exposure were eliminated.

Side View and CT Images: Non-standard views and non-X-ray images, such as CT slices, were remove.

Aspect Ratio Distortion: Cropped or zoom images that distorted the aspect ratio were correct or exclude.

Annotated Images: X-rays with visible annotations or markings were remove.

This dataset is sourced from Kaggle.

Chest X-Ray Pneumonia Dataset

This dataset contains chest x-ray images of independent patients that can be classified into normal (healthy) or pneumonia (diseased) patients. This dataset is a processed version of the original Large Dataset of Labeled Optical Coherence Tomography (OCT) and Chest X-Ray Images dataset provided by the University of California San Diego. The dataset contains three splits:

Train: 4187 images Validation: 1045 images Test: 624 images

The shape of the… See the full description on the dataset page: https://huggingface.co/datasets/mmenendezg/pneumonia_x_ray.

The proposed dataset has been combined from three popular lung segmentation datasets: Darwin, Montgomery, and Shenzhen. The combined data allow researchers and clinicians to gain access to a good quality dataset, a large proportion of which has been manually annotated. The combined dataset consists of 6,810 images, with corresponding binary masks of lungs with the following distribution of images between the three datasets: • 6,106 images from the Darwin dataset; • 139 images from the Montgomery dataset; • 566 images from the Shenzhen dataset.

The Darwin dataset [1, 2] images include most of the heart, revealing lung opacities behind the heart, which may be relevant for assessing the severity of viral pneumonia. The lower-most part of the lungs, where visible, is defined by the extent of the diaphragm. Where present and not obstructive to the distinguishability of the lungs, the diaphragm is included up until the lower-most visible part of the lungs. A key property of this dataset is that image resolutions, sources, and orientations vary across the dataset, with the smallest image being 156x156 pixels and the largest being 5600x4700 pixels. Furthermore, we included the portable X-ray images which are of significantly lower quality as compared to standard X-rays. A key limitation of the Darwin dataset is that it does not contain lateral X-ray lung segmentations. It is worth noting that lung segmentations were performed by human annotators using Darwin's Auto-Annotate AI and then adjusted and reviewed by expert radiologists.

Both the Montgomery and Shenzhen datasets [3] were published by the United States National Library of Medicine and are made of posteroanterior chest X-ray images. These images are available to foster research in computer-aided diagnosis of pulmonary diseases with a special focus on pulmonary tuberculosis. The datasets were acquired from the Department of Health and Human Services (Maryland, USA) and Shenzhen №3 People's Hospital (Shenzhen, China). Both datasets contain normal and abnormal chest X-ray images with manifestations of tuberculosis and include associated radiologist readings.

References: 1. Darwin’s Auto-Annotate AI. Available: https://www.v7labs.com/automated-annotation 2. COVID-19 X-ray dataset. Available: https://github.com/v7labs/covid-19-xray-dataset 3. Jaeger S, Candemir S, Antani S, Wáng Y-XJ, Lu P-X, Thoma G. Two public chest X-ray datasets for computer-aided screening of pulmonary diseases. Quant Imaging Med Surg. 2014;4: 475–477. doi:10.3978/j.issn.2223-4292.2014.11.20

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F13510457%2Fb90dc86ece01369e1ca4357da4941983%2Fimage_2.jpg?generation=1741792095479460&alt=media" alt="">

VERSION 7 UPDATED

DESCRIPTION:

The chest X-ray image dataset is sourced from Chest X-Ray Images (Pneumonia) including a total of 5,856 images for 02 classes, with the training set consisting of 5,232 images and the test set consisting of 624 images.

The dataset includes 03 directories: 1. chest-xray: original x-ray images 2. segment: segmented x-ray images (lungs isolated) 3. segment_with_convexhull: segmented x-ray images with convex hull (convex hull of lungs isolated)

Each of the directories above contains 02 subdirectories: train and test, which include 02 files: images.npy (containing images) and labels.npy (containing labels).

The images have been normalized with the following characteristics: - Each pixel value is in the range [0, 1]. - Each image has a size of (128, 128, 1) with one channel (grayscale).

The label of each image is either 0 (normal) or 1 (pneumonia).

=============================================================================

=============================================================================

MÔ TẢ:

Bộ dữ liệu ảnh chụp X-quang lồng ngực được lấy từ nguồn Chest X-Ray Images (Pneumonia) bao gồm tổng cộng 5856 ảnh cho 02 lớp, trong đó tập huấn luyện gồm 5232 ảnh và tập kiểm thử gồm 624 ảnh.

Bộ dữ liệu bao gồm 03 thư mục: 1. chest-xray: ảnh x-quang ban đầu 2. segment: ảnh x-quang được phân đoạn (vùng phổi được tách biệt) 3. segment_with_convexhull: ảnh x-quang được phân đoạn với vùng bao lồi (bao lồi vùng phổi được tách biệt)

Trong mỗi thư mục trên đều bao gồm 02 thư mục train và test, bao gồm 02 file images.npy (chứa ảnh) và labels.npy (chứa nhãn).

Ảnh đã được chuẩn hóa với các đặc điểm: - Giá trị mỗi pixel nằm trong khoảng [0, 1]. - Mỗi ảnh có kích thước (128, 128, 1) với 1 kênh màu (grayscale).

Nhãn của mỗi ảnh thuộc một trong hai giá trị 0 (bình thường) hoặc 1 (viêm phổi).

This classification dataset is from Kaggle and was uploaded to Kaggle by Paul Mooney.

It contains over 5,000 images of chest x-rays in two categories: "PNEUMONIA" and "NORMAL."

• Version 1 contains the raw images, and only has the pre-processing feature of "Auto-Orient" applied to strip out EXIF data, and ensure all images are "right side up."
• Version 2 contains the raw images with pre-processing features of "Auto-Orient" and Resize of 640 by 640 applied
• Version 3 was trained with Roboflow's model architecture for classification datasets and contains the raw images with pre-processing features of "Auto-Orient" and Resize of 640 by 640 applied + augmentations:
  • Outputs per training example: 3
  • Shear: ±3° Horizontal, ±2° Vertical
  • Saturation: Between -5% and +5%
  • Brightness: Between -5% and +5%
  • Exposure: Between -5% and +5%

Below you will find the description provided on Kaggle:

Context

http://www.cell.com/cell/fulltext/S0092-8674(18)30154-5 https://i.imgur.com/jZqpV51.png" alt="Figure S6"> Figure S6. Illustrative Examples of Chest X-Rays in Patients with Pneumonia, Related to Figure 6 The normal chest X-ray (left panel) depicts clear lungs without any areas of abnormal opacification in the image. Bacterial pneumonia (middle) typically exhibits a focal lobar consolidation, in this case in the right upper lobe (white arrows), whereas viral pneumonia (right) manifests with a more diffuse ‘‘interstitial’’ pattern in both lungs. http://www.cell.com/cell/fulltext/S0092-8674(18)30154-5

Content

The dataset is organized into 3 folders (train, test, val) and contains subfolders for each image category (Pneumonia/Normal). There are 5,863 X-Ray images (JPEG) and 2 categories (Pneumonia/Normal).

Chest X-ray images (anterior-posterior) were selected from retrospective cohorts of pediatric patients of one to five years old from Guangzhou Women and Children’s Medical Center, Guangzhou. All chest X-ray imaging was performed as part of patients’ routine clinical care.

For the analysis of chest x-ray images, all chest radiographs were initially screened for quality control by removing all low quality or unreadable scans. The diagnoses for the images were then graded by two expert physicians before being cleared for training the AI system. In order to account for any grading errors, the evaluation set was also checked by a third expert.

Acknowledgements

Data: https://data.mendeley.com/datasets/rscbjbr9sj/2

License: CC BY 4.0

Citation: http://www.cell.com/cell/fulltext/S0092-8674(18)30154-5 https://i.imgur.com/8AUJkin.png" alt="citation - latest version (Kaggle)">

Inspiration

Automated methods to detect and classify human diseases from medical images.

This dataset is a selection of the most relevant X-rays from the COVIDx-CXR version 8 database, representing three classes: COVID, PNEUMONIA, and NORMAL. The database contains 3,225 frontal chest radiographs with a balanced distribution of the three classes. The data was divided into training and test subsets, with 2,884 (89%) radiographs assigned to the training phase and 341 (11%) to the test phase.The chest radiographs were selected, and the annotations were verified by a radiologist to improve the selection criteria and database quality. The radiographs represent all stages of the diseases, from moderate to critical lesions, and include patients of different ages. This diversity was included to create a comprehensive database that accurately represents the general population and enhances the model's capability for generalization.COVIDx-CXR version 8 is one of the largest public databases developed as part of COVID-Net, a global open-source initiative dedicated to accelerating advances in machine learning to assist frontline healthcare workers in the fight against the pandemic Link : https://www.kaggle.com/datasets/andyczhao/covidx-cxr2?select=train.txtIt is constructed by combining various datasets from different sources :https://bimcv.cipf.es/bimcv-projects/bimcv-covid19/#1590858128006-9e640421-6711https://github.com/ml-workgroup/covid-19-image-repository/tree/master/pnghttps://sirm.org/category/senza-categoria/covid-19/https://eurorad.orghttps://github.com/ieee8023/covid-chestxray-datasethttps://figshare.com/articles/COVID-19_Chest_X-Ray_Image_Repository/12580328https://github.com/armiro/COVID-CXNethttps://www.kaggle.com/c/rsna-pneumonia-detection-challenge/datahttps://www.kaggle.com/paultimothymooney/chest-xray-pneumonia

This is a combined curated dataset of COVID-19 Chest X-ray images obtained by collating 15 publically available datasets as listed under the references section. The present dataset contains 1281 COVID-19 X-Rays, 3270 Normal X-Rays, 1656 viral-pneumonia X-Rays, and 3001 bacterial-pneumonia X-Rays. This dataset is developed as a part of the following research publication.

"A deep-learning based multimodal system for Covid-19 diagnosis using breathing sounds and chest X-ray images" https://doi.org/10.1016/j.asoc.2021.107522

The collected datasets—as cited by this dataset—are combined to form an integrated repository. This integrated repository contains a total of 4558 COVID-19 X-Rays, 5403 Normal X-Rays, 4497 Viral pneumonia X-Rays, and 5768 bacterial pneumonia X-Rays. Out of which 1379 COVID-19 X-Rays, 1476 normal X-Rays, 2690 viral pneumonia X-Rays, and 2588 bacterial pneumonia X-Rays are found to be duplicates—based on the image similarities—and thus are removed. Inception V3 architecture is used to obtain the image embeddings, which is followed by the use of unsupervised learning algorithms based on cosine similarity distances. These distances are clustered and then visualized to find different categories of image defects which are listed below:—

1.Noise 2.Pixelated 3.Compressed 4.Medical Implants 5.Washed out image 6.Side View 7.CT (sliced) image 8.Aspect Ratio distortion / Cropped / Zoomed 9.Rotated Images 10.Images with annotations

These clusters of defective images are removed during the curation process and a refined dataset is obtained which is available for download.

Dataset

This dataset was created by Pranav Raikote

Released under GPL 2

Contents

Dataset name Normal COVID-19 Pneumonia Total MOMA- Dataset 234 221 148 603

MOMA Dataset are collected from three resources, in the following links [1] https://github.com/smfai200/Detecting-COVID-19-in-X-ray-images/tree/master/dataset. Accessed at 19/4/2020 2.10 am. [2] https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia?fbclid=IwAR2jzVPSL8-MdEX8_DlFVrN EKJlu0nOUSlaKOxa4kOitv4RPeemEDRqL2E accessed at 27/4/2020 10.20 am.
[3] https://www.kaggle.com/bachrr/covid-chest-xray?fbclid=IwAR2kbLc1R3zqeC9lnBTAv5_lSB6XKVNGQlnilvH7uTI-M1rHGjJxYNLRb0k accessed at 27/4/2020 11.30 am.

Context

A team of researchers from Qatar University, Doha, Qatar, and the University of Dhaka, Bangladesh along with their collaborators from Pakistan and Malaysia in collaboration with medical doctors have created a database of chest X-ray images for COVID-19 positive cases along with Normal and Viral Pneumonia images. This COVID-19, normal and other lung infection dataset is released in stages. In the first release we have released 219 COVID-19, 1341 normal and 1345 viral pneumonia chest X-ray (CXR) images. In the first update, we have increased the COVID-19 class to 1200 CXR images. In the 2nd update, we have increased the database to 3616 COVID-19 positive cases along with 10,192 Normal, 6012 Lung Opacity (Non-COVID lung infection) and 1345 Viral Pneumonia images. We will continue to update this database as soon as we have new x-ray images for COVID-19 pneumonia patients.

Content

1. COVID-19 data:

COVID data are collected from different publicly accessible dataset, online sources and published papers. -2473 CXR images are collected from padchest dataset. -183 CXR images from a Germany medical school. -559 CXR image from SIRM, Github, Kaggle & Tweeter -400 CXR images from another Github source.

2. Normal images:

10192 Normal data are collected from from three different dataset. -8851 RSNA -1341 Kaggle

3. Lung opacity images:

6012 Lung opacity CXR images are collected from Radiological Society of North America (RSNA) CXR dataset

4. Viral Pneumonia images:

1345 Viral Pneumonia data are collected from the Chest X-Ray Images (pneumonia) database

Acknowledgements

Reference: [1]https://bimcv.cipf.es/bimcv-projects/bimcv-covid19/#1590858128006-9e640421-6711 [2]https://github.com/ml-workgroup/covid-19-image-repository/tree/master/png [3]https://sirm.org/category/senza-categoria/covid-19/ [4]https://eurorad.org [5]https://github.com/ieee8023/covid-chestxray-dataset [6]https://figshare.com/articles/COVID-19_Chest_X-Ray_Image_Repository/12580328 [7]https://github.com/armiro/COVID-CXNet
[8]https://www.kaggle.com/c/rsna-pneumonia-detection-challenge/data [9] https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia

Inspiration

Researchers can use this database to produce useful and impactful scholarly work on COVID-19, which can help in tackling this pandemic

ChestX-ray14 is a medical imaging dataset which comprises 112,120 frontal-view X-ray images of 30,805 (collected from the year of 1992 to 2015) unique patients with the text-mined fourteen common disease labels, mined from the text radiological reports via NLP techniques. It expands on ChestX-ray8 by adding six additional thorax diseases: Edema, Emphysema, Fibrosis, Pleural Thickening and Hernia.

This dataset contains anatomically segmented and color-enhanced chest X-ray (CXR) images, processed using the ASCE (Anatomical Segmentation and Color-Based Enhancement) pipeline. It includes 934 Normal, 1513 Viral Pneumonia, and 2587 Bacterial Pneumonia cases, originally sourced from Mendeley Data Repository, and used in the study titled “An Anatomically Enhanced and Clinically Validated Framework for Lung Abnormality Classification Using Deep Features and KL Divergence.”

ASCE-Enhanced Chest X-ray Dataset

This dataset contains color-enhanced and anatomically segmented chest X-ray images processed using the ASCE (Anatomical Segmentation and Color-Based Enhancement) framework. It includes three clinically relevant categories:
- Normal (934 images)
- Viral Pneumonia (1513 images)
- Bacterial Pneumonia (2587 images)

Origin and Attribution
This dataset is a derived work based on the original dataset published at:
[Mendeley Data - 8gf9vpkhgy](https://data.mendeley.com/datasets/8gf9vpkhgy/1)

Original Source Citation
> V. Labs, Darwin’s Auto-Annotate AI.
> "Lung Mask and Chest X-ray Data", Mendeley Data, V1.
> [https://data.mendeley.com/datasets/8gf9vpkhgy/1](https://data.mendeley.com/datasets/8gf9vpkhgy/1)

Description
Each image in this dataset has undergone:
1. Contrast and brightness adjustment
2. Histogram equalization
3. Bilateral filtering for texture preservation
4. Selective sharpening
5. Anatomical segmentation of:
- Blood Vessels (Red)
- Bronchial Tree (Green)
- Alveoli (Blue)

These enhanced images were used in the research titled:
"An anatomically enhanced and clinically validated framework for lung abnormality classification using deep features and KL divergence"

Folder Structure

ASCE_Enhanced_Renamed/
├── Normal/
├── Virus/
├── Bacteria/
├── metadata.csv
└── dataset_thumbnail.png

Pneumonia Detection with DenseNet121 🧠

This application uses a pre-trained DenseNet121 model to detect pneumonia from chest X-ray images.

  Features

Built with TensorFlow and Gradio Upload and get instant predictions Model trained on labeled X-ray dataset

  Instructions

Upload a chest X-ray image Wait for the prediction See if pneumonia is detected or not ✅🦠

🔗 Developed by Amir Ali

This study aimed to systematically diagnose pneumonia directly from paediatric chest X-ray images using a computational framework. The project research goals were 1) to establish a high-quality dataset of pneumonia labelled X-ray images, 2) to extend existing deep learning architectures for pneumonia diagnoses, and 3) to construct a computational pipeline, enabling members of the broader community to interface with the computational models to diagnose pneumonia from X-ray images. The paediatric chest x-ray images were derived from 1) a WHO-supported surveillance study at Dhaka Shishu Hospital (DSH), from 2) a community site at Kumudini Women's Medical College (KWMCH), and from 3) the WHO Chest Radiography in Epidemiological Studies (WHO CRES) working group. The images were interpreted by at least two trained clinicians / radiologists for the presence of 1) primary end-point pneumonia (PEP), 2) other lung infiltrates, and/or 3) pleural fluid in either the left or right lung, for a total of six possible binary outcomes, which will henceforth be called 'labels'. A third reader resolved discordant PEP labels found between the first and second readers. An X-ray image were included in this study if 1) the age of the child for whom the X-ray was performed was <=59 months, 2) the X-ray was performed in one of the two study hospitals or from the WHO CRES reference image set, and 3) the image captured the lung area. Any Image that was marked 'uninterpretable' (features of the images were not interpretable with respect to presence or absence of PEP) by two readers were excluded. The deposited datasets contain the resulting labels from the multiple readers for each dataset described above.