COVID19 XRAY DATA

More than 350 million cases of infection, 5 million fatalities, and ongoing negative effects on physical and mental health are all results of the COVID-19 pandemic, which has afflicted people all over the world. Globally, COVID-19 has caused a significant number of fatalities and now poses a novel and uncommon hazard to food security, labor and employment, and public health. Around 10 million individuals globally are impacted daily by COVID, according to the WHO, which is still spreading quickly. The most painful people are those who have chronic illnesses. In order to limit the rate at which the virus is spread by direct contact, researchers focus all of their efforts on reducing the infection rate and increasing the precision of disease detection. We contribute to accelerating the covid19 detection process for deep learning by creating a covid19 dataset in order to decrease the number of deaths caused by covid19. Our dataset, which numerous clinicians and researchers validated, comprises normal and covid19 X-ray pictures from various hospitals in Bangladesh. The dataset contains a total of 2000 images, where 1000 are normal and 1000 are covid images. Since the image quality is good and the data is sharp enough to extract image information, we anticipate that this dataset will aid in the diagnosis of covid19.

这场已波及全球的新型冠状病毒肺炎（COVID-19）大流行，已造成超3.5亿例感染病例、500万例死亡，且持续对人类身心健康造成负面影响。从全球维度来看，该疫情已造成大量死亡病例，且如今对粮食安全、劳动就业及公共卫生构成了全新且罕见的威胁。据世界卫生组织（WHO）统计，全球每日约有1000万人受到新冠疫情波及，且该病毒仍在快速传播。其中，慢性病患者所受影响最为深重。为限制病毒经直接接触传播的速率，研究人员全力致力于降低感染率、提升疾病检测的精准度。本研究通过构建新冠数据集，旨在加快深度学习用于新冠检测的流程，以降低新冠感染导致的死亡人数。本数据集经众多临床医师与研究人员验证，包含来自孟加拉国多家医院的正常胸部X线影像与新冠感染X线影像，总计2000张，其中正常影像与新冠感染影像各1000张。由于本数据集影像质量优良、画面清晰，足以支撑影像信息提取，我们预计该数据集将可为新冠感染的临床诊断提供有力支持。