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

@article{Abedi2022AHC,
  title={A High-Resolution Chest CT-Scan Image Dataset for COVID-19 Diagnosis and Differentiation},
  author={Iraj Abedi and Mahsa Vali and Bentolhoda Otroshi Shahreza and Hamidreza Bolhasani},
  journal={ArXiv},
  year={2022},
  volume={abs/2205.03408}
}
During the COVID-19 pandemic, computed tomography (CT) is a good way to diagnose 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. To address this problem, we have introduced HRCTv1-COVID-19… 

Figures from this paper

References

SHOWING 1-10 OF 24 REFERENCES

COVID-CT-Dataset: A CT Scan Dataset about COVID-19

An open-sourced dataset, which contains 349 COVID-19 CT images from 216 patients and 463 non-COVID- 19 CTs, is built, which is used to develop diagnosis methods based on multi-task learning and self-supervised learning that achieve an F1 of 0.90, an AUC of0.98, and an accuracy of 1.89.

COVID19-CT-dataset: an open-access chest CT image repository of 1000+ patients with confirmed COVID-19 diagnosis

An open-source repository containing 1000+ CT images of COVID-19 lung infections established by a team of board-certified radiologists and compressed and stored in RAR format is provided.

COVID-CT-MD, COVID-19 computed tomography scan dataset applicable in machine learning and deep learning

COVID-CT-MD dataset, consisting of not only COVID-19 cases, but also healthy and participants infected by Community Acquired Pneumonia (CAP), has the potential to facilitate the CO VID-19 research and can assist in development of advanced Machine Learning (ML) and Deep Neural Network (DNN) based solutions.

MosMedData: Chest CT Scans with COVID-19 Related Findings

This dataset contains anonymised human lung computed tomography (CT) scans with COVID-19 related findings, as well as without such findings. A small subset of studies has been annotated with binary

A systematic review of chest imaging findings in COVID-19.

Despite widespread use of CT in the diagnosis of COVID-19 patients based on the current literature, CT findings are not pathognomonic as it lacks specificity in differentiating imaging appearances caused by different types of pneumonia.

A Fully Automated Deep Learning-based Network For Detecting COVID-19 from a New And Large Lung CT Scan Dataset

A high-speed and accurate fully-automated method to detect COVID-19 from the patient's chest CT scan images is proposed and a novel architecture for improving the classification accuracy of convolutional networks on images containing small important objects is introduced.

Serial Quantitative Chest CT Assessment of COVID-19: A Deep Learning Approach

The quantification of lung opacification in COVID-19 measured at chest CT by using a commercially available deep learning–based tool was significantly different among groups with different clinical severity, which could potentially eliminate the subjectivity in the initial assessment and follow-up of pulmonary findings in CO VID-19.

Comparison between conventional interrupted high-resolution CT and volume multidetector CT acquisition in the assessment of bronchiectasis.

There is improved diagnostic accuracy and confidence for diagnosis and exclusion of bronchiectasis using 16-slice chest CT (1 mm cuts) compared with conventional HRCT of the chest.

COVID-19 pneumonia: a pictorial review of CT findings and differential diagnosis

The imaging findings in this viral pneumonia showed a broad spectrum, and there are no pathognomonic imaging findings for COVID-19 pneumonia.