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README.md
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- image-to-image
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tags:
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- medical
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dataset_info:
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features:
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- name: image
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dtype: string
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splits:
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- name: train
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num_bytes: 325782340
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num_examples: 97
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download_size: 313593688
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dataset_size: 325782340
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configs:
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- config_name: default
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data_files:
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- split: train
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path: data/train-*
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---
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# Chest X-ray - Object Detection dataset
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The dataset consists of a collection of chest X-ray images in **.jpg and .dcm** formats. The images are organized into folders based on different medical conditions related to the chest. Each folder contains images depicting specific chest abnormalities.
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#
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### Types of diseases and conditions in the dataset:
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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.
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# 💴
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# Content
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# Medical data might be collected in accordance with your requirements.
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More datasets in TrainingData's Kaggle account: **https://www.kaggle.com/trainingdatapro/datasets**
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TrainingData's GitHub: **https://github.com/trainingdata-pro**
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*keywords: lungs, heart, blood vessels, airways, ribs, lymph nodes, upper abdomen, cough, breath, structures, radiograph, scan, chest pain, fever, chest symptoms, chest dataset, chest X-rays dataset, scoliosis detection dataset, scoliosis segmentation dataset, scoliosis image dataset, medical imaging, radiology dataset, spine deformity dataset, orthopedic abnormalities, scoliotic curve dataset, degenerative spinal conditions, diagnostic imaging, medical imaging, radiology dataset, image dataset, abnormalities detection, chest anatomy, health, formations, imaging procedure, x-rays measurements, machine learning, computer vision, deep learning*
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- image-to-image
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tags:
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- medical
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- Chest X-Ray
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- Medicine
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- Computer Vision
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- Segmentation
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- Classification
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- Machine Learning
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dataset_info:
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features:
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- name: image
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dtype: string
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splits:
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- name: train
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num_bytes: 325782340
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num_examples: 97
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download_size: 313593688
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dataset_size: 325782340
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configs:
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- config_name: default
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data_files:
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- split: train
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path: data/train-*
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size_categories:
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- n<1K
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---
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# Chest X-ray - Object Detection dataset
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The dataset consists of a collection of chest X-ray images in **.jpg and .dcm** formats. The images are organized into folders based on different medical conditions related to the chest. Each folder contains images depicting specific chest abnormalities.
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# The dataset is created on the basis of [Chest X-Ray Dataset](https://unidata.pro/datasets/chest-x-ray-image-dicom/?utm_source=huggingface-td&utm_medium=referral&utm_campaign=chest-x-rays)
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### Types of diseases and conditions in the dataset:
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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.
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# 💴 For Commercial Usage: Full version of the dataset includes 400+ chest x-rays of people with different conditions, leave a request on **[our website](https://unidata.pro/datasets/chest-x-ray-image-dicom/?utm_source=huggingface-td&utm_medium=referral&utm_campaign=chest-x-rays)** to buy the dataset
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# Content
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# Medical data might be collected in accordance with your requirements.
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**🚀 You can learn more about our high-quality unique datasets [here](https://unidata.pro/datasets/chest-x-ray-image-dicom/?utm_source=huggingface-td&utm_medium=referral&utm_campaign=chest-x-rays)**
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*keywords: lungs, heart, blood vessels, airways, ribs, lymph nodes, upper abdomen, cough, breath, structures, radiograph, scan, chest pain, fever, chest symptoms, chest dataset, chest X-rays dataset, scoliosis detection dataset, scoliosis segmentation dataset, scoliosis image dataset, medical imaging, radiology dataset, spine deformity dataset, orthopedic abnormalities, scoliotic curve dataset, degenerative spinal conditions, diagnostic imaging, medical imaging, radiology dataset, image dataset, abnormalities detection, chest anatomy, health, formations, imaging procedure, x-rays measurements, machine learning, computer vision, deep learning*
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