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.

CheXpert is a dataset consisting of 224,316 chest radiographs of 65,240 patients who underwent a radiographic examination from Stanford University Medical Center between October 2002 and July 2017, in both inpatient and outpatient centers. The CheXpert dataset includes train, validation, and test sets. The validation and test sets include labels obtained by board-certified radiologists. The train set includes three sets of labels automatically extracted from associated radiology reports using various automated labelers (CheXpert, CheXbert, and VisualCheXbert).

IU X-ray (Demner-Fushman et al., 2016) is a set of chest X-ray images paired with their corresponding diagnostic reports. The dataset contains 7,470 pairs of images and reports.

Over 112,000 Chest X-ray images from more than 30,000 unique patients

This dataset is resized versions of images to 224x224. ![]() (1, Atelectasis; 2, Cardiomegaly; 3, Effusion; 4, Infiltration; 5, Mass; 6, Nodule; 7, Pneumonia; 8, Pneumothorax; 9, Consolidation; 10, Edema; 11, Emphysema; 12, Fibrosis; 13, Pleural_Thickening; 14 Hernia) ### Background & Motivation: Chest X-ray exam is one of the most frequent and cost-effective medical imaging examination. However clinical diagnosis of chest X-ray can be challenging, and sometimes believed to be harder than diagnosis via chest CT imaging. Even some promising work have been reported in the past, and especially in recent deep learning work on Tuberculosis (TB) classification. To achieve clinically relevant computer-aided detection and diagnosis (CAD) in real world medical sites on all data settings of chest X-rays is still very difficult, if not impossible when only several thousands of images are employed for study. This is evident from [2] where the performance deep neu

X-ray images in this data set have been acquired from the tuberculosis control program of the Department of Health andHuman Services of Montgomery County, MD, USA. This set contains 138 posterior-anterior x-rays, of which 80 x-rays are normal and 58 x-rays areabnormal with manifestations of tuberculosis. All images are de-identified and available in DICOM format. The set covers a wide range of abnormalities,including effusions and miliary patterns. The data set includes radiology readings available as a text files and summary of its content

The dataset is organized into 3 folders (covid, pneumonia , normal) and metadata.csv which contain chest X-ray posteroanterior (PA) images. X-ray samples of COVID-19 were retrieved from different sources for the unavailability of a large specific dataset. First, 613 X-ray images of COVID-19 cases were collected from the following websites: GitHub [1,2], Radiopaedia [3], The Cancer Imaging Archive (TCIA) [4], and the Italian Society of Radiology (SIRM) [5]. Then, instead of data being independently augmented, a dataset containing 912 already augmented images was collected from Mendeley [6]. Finally, 1525 images of pneumonia cases and 1525 X-ray images of normal cases were collected from the Kaggle repository [7] and NIH dataset [8]. A total of 4575 samples were used in the experiment, where 1525 samples were used for each case.

References: [1] http://arxiv.org/abs/2003.11597 [2] https://github.com/agchung [3] https://radiopaedia.org/ [4] https://www.cancerimagingarchive.net/ [5] https://www.sirm.org/en/category/articles/covid-19-database/ [6] https://data.mendeley.com/datasets/2fxz4px6d8/4 [7] https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia [8] https://www.kaggle.com/nih-chest-xrays/data

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.

CURATED COVID-19 CHEST X-RAY DATASET

***DISCLAIMER: The dataset uploaded here is already partitioned and preprocessed compared to the original. ***

The following data contain images labeled with Normal, COVID-19, and Pneumonia infected chest x-rays.

This dataset came from:

SAIT, UNAIS; k v, Gokul Lal; Prajapati, Sunny; Bhaumik, Rahul; Kumar, Tarun; S, Sanjana; Bhalla , Kriti (2020), “Curated Dataset for COVID-19 Posterior-Anterior Chest Radiography Images (X-Rays).”, Mendeley Data, V1, doi: 10.17632/9xkhgts2s6.1

Please consider citing upon use.

This dataset is also preprocessed and splitted specifically for the evaluation of these papers:

F. J. P. Montalbo, "Truncating a Densely Connected Convolutional Neural Network with Partial Layer Freezing and Feature Fusion for Diagnosing COVID-19 from Chest X-Rays," MethodsX, vol. 8, 101408, 2021. doi: 10.1016/j.mex.2021.101408.

F. J. P. Montalbo, "Diagnosing Covid-19 Chest X-Rays with a Lightweight Truncated DenseNet with Partial Layer Freezing and Feature Fusion," Biomedical Signal Processing and Control (BSPC), vol. 68,102583, 2021. doi: 10.1016/j.bspc.2021.102583.

The authors of the ZhanLabData: Large Dataset of Labeled Optical Coherence Tomography (OCT) and Chest X-Ray Images addressed challenges related to reliability and interpretability in the implementation of clinical-decision support algorithms for medical imaging. The Chest XRay part has a total of 5,856 patients contributed to the dataset, with 4,273 images characterized as depicting PNEUMONIA_BACTERIA and PNEUMONIA_VIRUS (rest - NORMAL images). They established a diagnostic tool based on a deep-learning framework specifically designed for the screening of patients with common treatable blinding retinal diseases.

Chest X-ray exams are one of the most frequent and cost-effective medical imaging examinations available. However, clinical diagnosis of a chest X-ray can be challenging and sometimes more difficult than diagnosis via chest CT imaging. The lack of large publicly available datasets with annotations means it is still very difficult, if not impossible, to achieve clinically relevant computer-aided detection and diagnosis (CAD) in real world medical sites with chest X-rays. One major hurdle in creating large X-ray image datasets is the lack resources for labeling so many images. Prior to the release of this dataset, Openi was the largest publicly available source of chest X-ray images with 4,143 images available. This NIH Chest X-ray Dataset is comprised of 112,120 X-ray images with disease labels from 30,805 unique patients. To create these labels, the authors used Natural Language Processing to text-mine disease classifications from the associated radiological reports. The labels are expected to be >90% accurate and suitable for weakly-supervised learning. The original radiology reports are not publicly available but you can find more details on the labeling process in this Open Access paper: "ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases." (Wang et al.)

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.

Open access chest X-ray collection from Indiana University

Original source: https://openi.nlm.nih.gov/

Original images were downloaded in raw DICOM standard. Each image was converted to png using some post-processing: - top/bottom 0.5% DICOM pixel values were clipped (to eliminate very dark or very bright pixel outliers) - DICOM pixel values scaled linearly to fit into 0-255 range - resized to 2048 on shorter side (to fit in Kaggle dataset limits)

Metadata downloaded using available API (https://openi.nlm.nih.gov/services#searchAPIUsingGET)

Each image classified manually into frontal and lateral chest X-ray categories.

Dataset Labels

['NORMAL', 'PNEUMONIA']

  Number of Images

{'train': 4077, 'test': 582, 'valid': 1165}

  How to Use

Install datasets:

pip install datasets

Load the dataset:

from datasets import load_dataset

ds = load_dataset("keremberke/chest-xray-classification", name="full") example = ds['train'][0]

  Roboflow Dataset Page

https://universe.roboflow.com/mohamed-traore-2ekkp/chest-x-rays-qjmia/dataset/2

  Citation… See the full description on the dataset page: https://huggingface.co/datasets/keremberke/chest-xray-classification.

Chest X-ray(CXR) images are prominent among medical images and are commonly administered in emergency diagnosis and treatment corresponding to cardiac and respiratory diseases. Though there are robust solutions available for medical diagnosis, validation of artificial intelligence (AI) in radiology is still questionable. Segmentation is pivotal in chest radiographs that aid in improvising the existing AI-based medical diagnosis process. We provide the CXLSeg dataset: Chest X-ray with Lung Segmentation, a comparatively large dataset of segmented Chest X-ray radiographs based on the MIMIC-CXR dataset, a popular CXR image dataset. The dataset contains segmentation results of 243,324 frontal view images of the MIMIC-CXR dataset and corresponding masks. Additionally, this dataset can be utilized for computer vision-related deep learning tasks such as medical image classification, semantic segmentation and medical report generation. Models using segmented images yield better results since only the features related to the important areas of the image are focused. Thus images of this dataset can be manipulated to any visual feature extraction process associated with the original MIMIC-CXR dataset and enhance the results of the published or novel investigations. Furthermore, masks provided by this dataset can be used to train segmentation models when combined with the MIMIC-CXR-JPG dataset. The SA-UNet model achieved a 96.80% in dice similarity coefficient and 91.97% in IoU for lung segmentation using CXLSeg.

• 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

Medical Symptoms are sometimes very tricky to analyse in real time as it takes time for example, first to detect the symptoms then to perform some tests and finally coming to a solution. This process can be eliminated and lot of time can be saved by introducing the concept of Deep learning. CNNs create a network for extracting the features of a given image in order to evaluate the image based on the conditions required. This property of the CNN is used as a certain advantage in order to detect the symptoms based on the type of X-ray images provided.

National Institutes of Health Chest X-Ray Dataset

https://www.kaggle.com/nih-chest-xrays

Chest X-ray exams are one of the most frequent and cost-effective medical imaging examinations available. However, clinical diagnosis of a chest X-ray can be challenging and sometimes more difficult than diagnosis via chest CT imaging. The lack of large publicly available datasets with annotations means it is still very difficult, if not impossible, to achieve clinically relevant computer-aided detection and diagnosis (CAD) in real world medical sites with chest X-rays. One major hurdle in creating large X-ray image datasets is the lack resources for labeling so many images. Prior to the release of this dataset, Openi was the largest publicly available source of chest X-ray images with 4,143 images available.

This NIH Chest X-ray Dataset is comprised of 112,120 X-ray images with disease labels from 30,805 unique patients. To create these labels, the authors used Natural Language Processing to text-mine disease classifications from the associated radiological reports. The labels are expected to be >90% accurate and suitable for weakly-supervised learning. The original radiology reports are not publicly available but you can find more details on the labeling process in this Open Access paper: "ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases." (Wang et al.)

Released UnderCC0: Public Domain

https://www.kaggle.com/nih-chest-xrays/data

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.