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

Autor:	Abedi, Iraj, Vali, Mahsa, Otroshi, Bentolhoda, Zamanian, Maryam, Bolhasani, Hamidreza
Rok vydání:	2022
Předmět:	Electrical Engineering and Systems Science - Image and Video Processing Computer Science - Computer Vision and Pattern Recognition Physics - Medical Physics
Druh dokumentu:	Working Paper
Popis:	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 Comment: 5 pages, 2 figures and 1 table
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2205.03408 Zobrazit plný text záznamu View this record from Arxiv