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.新冠疫情（COVID-19）已波及全球各国民众，累计造成超3.5亿感染病例、500万死亡病例，并持续对人们的身心健康造成负面影响。 新冠疫情在全球范围内造成了大量人员死亡，如今更对粮食安全、劳动就业与公共卫生构成了新型且罕见的威胁。据世界卫生组织（WHO）统计，新冠仍在快速传播，全球每日约有1000万人受到疫情影响，罹患慢性病的群体受创最为深重。 为通过限制直接接触以减缓病毒传播速度，研究人员全力致力于降低感染率、提升疾病检测的精准度。 为降低新冠疫情导致的死亡人数，我们构建了新冠（COVID-19）检测数据集，以助力加速深度学习用于新冠检测的流程。本数据集经众多临床医师与研究人员验证，收录了孟加拉国多家医院拍摄的正常X线（X-ray）影像与新冠阳性X线影像。数据集总计包含2000张图像，其中正常影像与新冠阳性影像各1000张。由于本数据集图像质量优良、细节清晰足以提取有效图像信息，我们期望该数据集能够为新冠诊断提供助力。