HRCTCov19 -- A High-Resolution Chest CT Scan Image Dataset for COVID-19
Diagnosis and Differentiation
- URL: http://arxiv.org/abs/2205.03408v3
- Date: Tue, 5 Dec 2023 10:30:12 GMT
- Title: HRCTCov19 -- A High-Resolution Chest CT Scan Image Dataset for COVID-19
Diagnosis and Differentiation
- Authors: Iraj Abedi, Mahsa Vali, Bentolhoda Otroshi, Maryam Zamanian, Hamidreza
Bolhasani
- Abstract summary: During the COVID-19 pandemic, computed tomography (CT) was a popular method for diagnosing COVID-19 patients.
Publicly accessible COVID-19 CT image datasets are difficult to come by due to privacy concerns.
We have introduced HRCTCov19, a new COVID-19 high-resolution chest CT scan image dataset.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Introduction: During the COVID-19 pandemic, computed tomography (CT) was a
popular method for diagnosing COVID-19 patients. HRCT (High-Resolution Computed
Tomography) is a form of computed tomography that uses advanced methods to
improve image resolution. Publicly accessible COVID-19 CT image datasets are
very difficult to come by due to privacy concerns, which impedes the study and
development of AI-powered COVID-19 diagnostic algorithms based on CT images.
Data description: To address this problem, we have introduced HRCTCov19, a new
COVID-19 high-resolution chest CT scan image dataset that includes not only
COVID-19 cases of Ground Glass Opacity (GGO), Crazy Paving, and Air Space
Consolidation but also CT images of cases with negative COVID-19. The HRCTCov19
dataset, which includes slice-level, and patient-level labels, has the
potential to aid COVID-19 research, especially for diagnosis and
differentiation using artificial intelligence algorithms, machine learning, and
deep learning methods. This dataset is accessible through the web at:
http://databiox.com and includes 181,106 chest HRCT images from 395 patients
with four labels: GGO, Crazy Paving, Air Space Consolidation, and Negative.
Keywords: COVID-19, CT scan, Computed Tomography, Chest Image, Dataset, Medical
Imaging
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