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Hrctcov19-A High-Resolution Chest Ct Scan Image Dataset for Covid-19 Diagnosis and Differentiation Publisher Pubmed



Abedi I1 ; Vali M2 ; Otroshi B3 ; Zamanian M1 ; Bolhasani H4
Authors
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Authors Affiliations
  1. 1. Department of Medical Physics, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran
  2. 2. Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan, Iran
  3. 3. Department of Radiology, School of Medicine, Arak University of Medical Sciences, Arak, Iran
  4. 4. Department of Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran

Source: BMC Research Notes Published:2024


Abstract

Introduction: Computed tomography (CT) was a widely used diagnostic technique for COVID-19 during the pandemic. High-Resolution Computed Tomography (HRCT), is a type of computed tomography that enhances image resolution through the utilization of advanced methods. Due to privacy concerns, publicly available COVID-19 CT image datasets are incredibly tough to come by, leading to it being challenging to research and create AI-powered COVID-19 diagnostic algorithms based on CT images. Data description: To address this issue, we created HRCTCov19, a new COVID-19 high-resolution chest CT scan image collection 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 labeling, has the potential to assist in COVID-19 research, in particular for diagnosis and a distinction using AI algorithms, machine learning, and deep learning methods. This dataset, which can be accessed through the web at http://databiox.com , includes 181,106 chest HRCT images from 395 patients labeled as GGO, Crazy Paving, Air Space Consolidation, and Negative. © 2024, The Author(s).
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