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The novel Coronavirus Disease 2019 (COVID-19) is a global pandemic disease spreading rapidly around the world. A robust and automatic early recognition of COVID-19, via auxiliary computer-aided diagnostic tools, is essential for disease…

图像与视频处理 · 电气工程与系统科学 2020-07-24 Md. Kamrul Hasan , Md. Ashraful Alam , Md. Toufick E Elahi , Shidhartho Roy , Sifat Redwan Wahid

The rapid outbreak of COVID-19 threatens humans life all around the world. Due to insufficient diagnostic infrastructures, developing an accurate, efficient, inexpensive, and quick diagnostic tool is of great importance. As chest…

图像与视频处理 · 电气工程与系统科学 2021-01-01 Maryam Dialameh , Ali Hamzeh , Hossein Rahmani , Amir Reza Radmard , Safoura Dialameh

Automatic lung lesions segmentation of chest CT scans is considered a pivotal stage towards accurate diagnosis and severity measurement of COVID-19. Traditional U-shaped encoder-decoder architecture and its variants suffer from diminutions…

图像与视频处理 · 电气工程与系统科学 2020-12-04 Tanvir Mahmud , Md Awsafur Rahman , Shaikh Anowarul Fattah , Sun-Yuan Kung

Supervised learning method requires a large volume of annotated datasets. Collecting such datasets is time-consuming and expensive. Until now, very few annotated COVID-19 imaging datasets are available. Although self-supervised learning…

图像与视频处理 · 电气工程与系统科学 2020-12-14 Li Sun , Ke Yu , Kayhan Batmanghelich

The coronavirus (COVID-19) is currently the most common contagious disease which is prevalent all over the world. The main challenge of this disease is the primary diagnosis to prevent secondary infections and its spread from one person to…

In here, we introduce a novel approach to enhance the accuracy and efficiency of COVID-19 diagnosis using CT images. Leveraging state-of-the-art Transformer models in computer vision, we employed the base ViT Transformer configured for…

图像与视频处理 · 电气工程与系统科学 2023-11-29 Kenan Morani

In recent years, deep learning-based image analysis methods have been widely applied in computer-aided detection, diagnosis and prognosis, and has shown its value during the public health crisis of the novel coronavirus disease 2019…

Automatic segmentation of lung lesions associated with COVID-19 in CT images requires large amount of annotated volumes. Annotations mandate expert knowledge and are time-intensive to obtain through fully manual segmentation methods.…

图像与视频处理 · 电气工程与系统科学 2023-09-06 Muhammad Asad , Lucas Fidon , Tom Vercauteren

This paper addresses the new problem of automated screening of coronavirus disease 2019 (COVID-19) based on chest X-rays, which is urgently demanded toward fast stopping the pandemic. However, robust and accurate screening of COVID-19 from…

图像与视频处理 · 电气工程与系统科学 2020-05-22 Tianyang Li , Zhongyi Han , Benzheng Wei , Yuanjie Zheng , Yanfei Hong , Jinyu Cong

COVID-19 has become a global pandemic and is still posing a severe health risk to the public. Accurate and efficient segmentation of pneumonia lesions in CT scans is vital for treatment decision-making. We proposed a novel unsupervised…

图像与视频处理 · 电气工程与系统科学 2021-11-24 Chengyijue Fang , Yingao Liu , Mengqiu Liu , Xiaohui Qiu , Ying Liu , Yang Li , Jie Wen , Yidong Yang

Real-time detection of COVID-19 using radiological images has gained priority due to the increasing demand for fast diagnosis of COVID-19 cases. This paper introduces a novel two-phase approach for classifying chest X-ray images. Deep…

图像与视频处理 · 电气工程与系统科学 2021-06-04 Hu Tianqing , Mohammad Khishe , Mokhtar Mohammadi , Gholam-Reza Parvizi , Sarkhel H. Taher Karim , Tarik A. Rashid

The Coronavirus Disease 2019 (COVID-19) is affecting increasingly large number of people worldwide, posing significant stress to the health care systems. Early and accurate diagnosis of COVID-19 is critical in screening of infected patients…

图像与视频处理 · 电气工程与系统科学 2020-10-28 Jianjia Zhang

In this paper, a 3D-RegNet-based neural network is proposed for diagnosing the physical condition of patients with coronavirus (Covid-19) infection. In the application of clinical medicine, lung CT images are utilized by practitioners to…

图像与视频处理 · 电气工程与系统科学 2021-07-12 Haibo Qi , Yuhan Wang , Xinyu Liu

Deep learning methods provide significant assistance in analyzing coronavirus disease (COVID-19) in chest computed tomography (CT) images, including identification, severity assessment, and segmentation. Although the earlier developed…

图像与视频处理 · 电气工程与系统科学 2022-03-29 Stanislav Shimovolos , Andrey Shushko , Mikhail Belyaev , Boris Shirokikh

Supervised deep learning-based methods yield accurate results for medical image segmentation. However, they require large labeled datasets for this, and obtaining them is a laborious task that requires clinical expertise.…

计算机视觉与模式识别 · 计算机科学 2021-12-20 Krishna Chaitanya , Ertunc Erdil , Neerav Karani , Ender Konukoglu

Coronavirus, or COVID-19, is a hazardous disease that has endangered the health of many people around the world by directly affecting the lungs. COVID-19 is a medium-sized, coated virus with a single-stranded RNA, and also has one of the…

The newly discovered Corona virus Disease 2019 (COVID-19) has been globally spreading and causing hundreds of thousands of deaths around the world as of its first emergence in late 2019. Computed tomography (CT) scans have shown distinctive…

With the massive damage in the world caused by Coronavirus Disease 2019 SARS-CoV-2 (COVID-19), many related research topics have been proposed in the past two years. The Chest Computed Tomography (CT) scans are the most valuable materials…

图像与视频处理 · 电气工程与系统科学 2021-07-13 Chih-Chung Hsu , Guan-Lin Chen , Mei-Hsuan Wu

Analysis of chest CT scans can be used in detecting parts of lungs that are affected by infectious diseases such as COVID-19.Determining the volume of lungs affected by lesions is essential for formulating treatment recommendations and…

Coronavirus Disease 2019 (COVID-19) pandemic rapidly spread globally, impacting the lives of billions of people. The effective screening of infected patients is a critical step to struggle with COVID-19, and treating the patients avoiding…

图像与视频处理 · 电气工程与系统科学 2024-12-30 Leonardo Gabriel Ferreira Rodrigues , Danilo Ferreira da Silva , Larissa Ferreira Rodrigues , João Fernando Mari
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