PulDi-COVID: Chronic obstructive pulmonary (lung) diseases with COVID-19 classification using ensemble deep convolutional neural network from chest X-ray images to minimize severity and mortality rates [0.03%]
PulDi-COVID：使用集成深度卷积神经网络从胸部X光片对慢性阻塞性肺疾病（肺）与COVID-19进行分类以最小化严重程度和死亡率
Yogesh H Bhosale,K Sridhar Patnaik
Yogesh H Bhosale
Background and objective: In the current COVID-19 outbreak, efficient testing of COVID-19 individuals has proven vital to limiting and arresting the disease's accelerated spread globally. It has been observed that the sev...
The SEIR model incorporating asymptomatic cases, behavioral measures, and lockdowns: Lesson learned from the COVID-19 flow in Sweden [0.03%]
结合无症状病例、行为措施和封锁的SEIR模型：从瑞典的COVID-19疫情中得到的经验教训
Muhamad Khairulbahri
Muhamad Khairulbahri
The Sweden approach is unique in handling the COVID-19 flow, compared to other European countries. While other countries have practiced the full lockdowns, Sweden has practiced the lighter lockdowns or the partial lockdowns as public spaces...
Optimal Ensemble learning model for COVID-19 detection using chest X-ray images [0.03%]
使用胸部X光图像进行COVID-19检测的最优集成学习模型
S Balasubramaniam,K Satheesh Kumar
S Balasubramaniam
COVID-19 pandemic is the main outbreak in the world, which has shown a bad impact on people's lives in more than 150 countries. The major steps in fighting COVID-19 are identifying the affected patients as early as possible and locating the...
Cov-TransNet: Dual branch fusion network with transformer for COVID-19 infection segmentation [0.03%]
Cov-TransNet：用于COVID-19感染分割的具有转换器的双分支融合网络
Yanjun Peng,Tong Zhang,Yanfei Guo
Yanjun Peng
Segmentation of COVID-19 infection is a challenging task due to the blurred boundaries and low contrast between the infected and the non-infected areas in COVID-19 CT images, especially for small infection regions. COV-TransNet is presented...
Automated diagnosis of COVID-19 using radiological modalities and Artificial Intelligence functionalities: A retrospective study based on chest HRCT database [0.03%]
基于胸部高分辨率CT数据库的COVID-19放射学诊断方法的自动化研究：一项回顾性研究
Upasana Bhattacharjya,Kandarpa Kumar Sarma,Jyoti Prakash Medhi et al.
Upasana Bhattacharjya et al.
Background and objective: The spread of coronavirus has been challenging for the healthcare system's proper management and diagnosis during the rapid spread and control of the infection. Real-time reverse transcription-po...
New pulse oximetry detection based on the light absorbance ratio as determined from amplitude modulation indexes in the time and frequency domains [0.03%]
基于时域和频域调制指数确定的光吸收比的新脉搏血氧检测方法
Pattana Kainan,Ananta Sinchai,Panwit Tuwanut et al.
Pattana Kainan et al.
The Pandemic COVID-19 situation, a pulse Oximetry is significant to detect a varying blood oxygen saturation of a patient who needed the device to operate with continuous, rapid, high accuracy, and immune of moving artifacts. In this articl...
Deep learning models-based CT-scan image classification for automated screening of COVID-19 [0.03%]
基于深度学习模型的CT扫描图像分类用于自动筛查COVID-19疫情
Kapil Gupta,Varun Bajaj
Kapil Gupta
COVID-19 is the most transmissible disease, caused by the SARS-CoV-2 virus that severely infects the lungs and the upper respiratory tract of the human body. This virus badly affected the lives and wellness of millions of people worldwide a...
A teacher-student framework with Fourier Transform augmentation for COVID-19 infection segmentation in CT images [0.03%]
一种基于傅立叶变换增强的教师-学生框架用于CT图像中新冠肺炎感染区域分割
Han Chen,Yifan Jiang,Hanseok Ko et al.
Han Chen et al.
Automatic segmentation of infected regions in computed tomography (CT) images is necessary for the initial diagnosis of COVID-19. Deep-learning-based methods have the potential to automate this task but require a large amount of data with p...
Identification and classification of coronavirus genomic signals based on linear predictive coding and machine learning methods [0.03%]
基于线性预测编码和机器学习方法的身份验证和分类冠状病毒基因组信号
Amin Khodaei,Parvaneh Shams,Hadi Sharifi et al.
Amin Khodaei et al.
Corona disease has become one of the problems and challenges of humankind over the past two years. One of the problems that existed from the first days of this epidemic was clinical symptoms similar to other infectious viruses such as colds...
A texture-aware U-Net for identifying incomplete blinking from eye videography [0.03%]
一种基于U-Net的眨眼检测算法
Qinxiang Zheng,Xin Zhang,Juan Zhang et al.
Qinxiang Zheng et al.
Accurate identification of incomplete blinking from eye videography is critical for the early detection of eye disorders or diseases (e.g., dry eye). In this study, we develop a texture-aware neural network based on the classical U-Net (ter...