image
imagewidth (px) 690
705
|
|---|
Chest X-ray
The dataset consists of .dcm files containing X-ray images of the thorax. The images are labeled by the doctors and accompanied by corresponding annotations in JSON format. The annotations provide detailed information about the organ structures present in the chest X-ray images.
💴 For Commercial Usage: Full version of the dataset includes 400+ chest x-rays of people with different conditions, leave a request on TrainingData to buy the dataset
Types of diseases and conditions in the dataset:
- Petrifications
- Nodule/mass
- Infiltration/Consolidation
- Fibrosis
- Dissemination
- Pleural effusion
- Hilar enlargement
- Annular shadows
- Healed rib fracture
- Enlarged medinastium
- Rib fractures
- Pneumothorax
- Atelectasis
Statistics for the dataset:
The dataset aims to aid in the development and evaluation of algorithms for automated detection and classification of thoracic organ abnormalities and diseases.
The dataset is valuable for research in neurology, radiology, and oncology. It allows the development and evaluation of computer-based algorithms, machine learning models, and deep learning techniques for automated detection, diagnosis, and classification of these conditions.
💴 Buy the Dataset: This is just an example of the data. Leave a request on https://trainingdata.pro/datasets to discuss your requirements, learn about the price and buy the dataset
Content
The dataset includes:
- files: includes x-ray scans in .dcm format,
- annotations: includes annotations in JSON format made for files in the previous folder,
- visualizations: includes visualizations of the annotations,
- .csv file: includes links to the fies and metadata
File with the extension .csv includes the following information for each media file:
- dcm_path: link to access the .dcm file,
- annotation_path: link to access the file with annotation in JSON-format,
- age: age of the person in the x-ray scan,
- sex: gender of the person in the x-ray scan,
- StudyInstanceUID: id of the study,
- Nodule/mass: wheter nodule/mass is observed,
- Dissemination: wheter dissemination is observed,
- Annular shadows: wheter annular shadows are observed,
- Petrifications: wheter petrifications are observed,
- Pleural effusion: wheter pleural effusion is observed
Medical data might be collected in accordance with your requirements.
TrainingData
More datasets in TrainingData's Kaggle account: https://www.kaggle.com/trainingdatapro/datasets
TrainingData's GitHub: https://github.com/Trainingdata-datamarket/TrainingData_All_datasets
keywords: ibeta level 1, ibeta level 2, liveness detection systems, liveness detection dataset, biometric dataset, biometric data dataset, biometric system attacks, anti-spoofing dataset, face liveness detection, deep learning dataset, face spoofing database, face anti-spoofing, face recognition, face detection, face identification, human video dataset, video dataset, presentation attack detection, presentation attack dataset, 2d print attacks, print 2d attacks dataset, printed 2d masks dataset, spoofing in 2D face recognition, facial masks, 2D face recognition systems, detecting face spoofing attacks, detecting presentation attacks, computer vision, surveillance face anti-spoofing, face liveness detection software solution, silicone masks attacks
- Downloads last month
- 0