HRCTCov19 -- A High-Resolution Chest CT Scan Image Dataset for COVID-19 Diagnosis and Differentiation

TL;DR

The paper introduces HRCTCov19, a large high-resolution chest CT dataset with labeled COVID-19 and non-COVID cases, aimed at facilitating AI-based diagnosis and differentiation research.

Contribution

It provides a publicly accessible, large-scale, high-resolution COVID-19 chest CT dataset with detailed labels, addressing privacy issues and supporting AI development.

Findings

Dataset contains 181,106 images from 395 patients.

Includes labels for GGO, Crazy Paving, Air Space Consolidation, and Negative.

Enables improved AI-based COVID-19 diagnosis and differentiation.

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