MLCA2F: Multi-Level Context Attentional Feature Fusion for COVID-19 lesion segmentation from CT scans [0.03%]
用于从CT图像中分割COVID-19病灶的多级上下文注意力特征融合方法
Ibtissam Bakkouri,Karim Afdel
Ibtissam Bakkouri
In the field of diagnosis and treatment planning of Coronavirus disease 2019 (COVID-19), accurate infected area segmentation is challenging due to the significant variations in the COVID-19 lesion size, shape, and position, boundary ambigui...
Attention-augmented U-Net (AA-U-Net) for semantic segmentation [0.03%]
注意力增强U-Net（AA-U-Net）在语义分割中的应用
Kumar T Rajamani,Priya Rani,Hanna Siebert et al.
Kumar T Rajamani et al.
Deep learning-based image segmentation models rely strongly on capturing sufficient spatial context without requiring complex models that are hard to train with limited labeled data. For COVID-19 infection segmentation on CT images, trainin...
Unconstrained face mask and face-hand interaction datasets: building a computer vision system to help prevent the transmission of COVID-19 [0.03%]
无约束口罩人脸和戴手套手势数据集：建立计算机视觉系统以帮助预防COVID-19的传播
Fevziye Irem Eyiokur,Hazım Kemal Ekenel,Alexander Waibel
Fevziye Irem Eyiokur
Health organizations advise social distancing, wearing face mask, and avoiding touching face to prevent the spread of coronavirus. Based on these protective measures, we developed a computer vision system to help prevent the transmission of...
Deep convolutional neural networks for detection of abnormalities in chest X-rays trained on the very large dataset [0.03%]
基于超大型数据集训练的深度卷积神经网络在胸部X光片异常检测中的应用研究
Kadir Aktas,Vuk Ignjatovic,Dragan Ilic et al.
Kadir Aktas et al.
One of the main challenges in the current pandemic is the detection of coronavirus. Conventional techniques (PT-PCR) have their limitations such as long response time and limited accessibility. On the other hand, X-ray machines are widely a...
FCML-gait: fog computing and machine learning inspired human identity and gender recognition using gait sequences [0.03%]
基于雾计算和机器学习的行走序列人体身份及性别识别（FCML-gait）
Khalil Ahmed,Munish Saini
Khalil Ahmed
Security threats are always there if the human intruders are not identified and recognized well in time in highly security-sensitive environments like the military, airports, parliament houses, and banks. Fog computing and machine learning ...
A novel traffic accident detection method with comprehensive traffic flow features extraction [0.03%]
一种基于全面交通流量特征提取的新型交通事故检测方法
Liping Zhu,Bingyao Wang,Yihan Yan et al.
Liping Zhu et al.
With the rapidly increasing of automobiles, traffic accidents are gradually becoming more frequent. This creates a great need for effective traffic anomaly detection algorithms. Existing methods shed light on directly inferring the abnormal...
A comparative study of medical image enhancement algorithms and quality assessment metrics on COVID-19 CT images [0.03%]
基于新冠肺炎CT图像的医学影像增强算法及质量评价指标的对比研究
Muhammad Waqar Mirza,Asif Siddiq,Ishtiaq Rasool Khan
Muhammad Waqar Mirza
Medical imaging can help doctors in better diagnosis of several conditions. During the present COVID-19 pandemic, timely detection of novel coronavirus is crucial, which can help in curing the disease at an early stage. Image enhancement te...
EfficientMask-Net for face authentication in the era of COVID-19 pandemic [0.03%]
面向新冠疫情时代的口罩人脸认证方法 EfficientMask-Net
Neda Azouji,Ashkan Sami,Mohammad Taheri
Neda Azouji
Today, we are facing the COVID-19 pandemic. Accordingly, properly wearing face masks has become vital as an effective way to prevent the rapid spread of COVID-19. This research develops an Efficient Mask-Net method for low-power devices, su...
A Novel Threshold-Based Segmentation Method for Quantification of COVID-19 Lung Abnormalities [0.03%]
一种基于阈值的分割方法量化COVID-19肺异常的新方法
Azrin Khan,Rachael Garner,Marianna La Rocca et al.
Azrin Khan et al.
Since December 2019, the novel coronavirus disease 2019 (COVID-19) has claimed the lives of more than 3.75 million people worldwide. Consequently, methods for accurate COVID-19 diagnosis and classification are necessary to facilitate rapid ...
An adaptive feature extraction method for classification of Covid-19 X-ray images [0.03%]
一种针对COVID-19 X光图像分类的自适应特征提取方法
Zeynep Gündoğar,Furkan Eren
Zeynep Gündoğar
This study aims to detect Covid-19 disease in the fastest and most accurate way from X-ray images by developing a new feature extraction method and deep learning model . Partitioned Tridiagonal Enhanced Multivariance Products Representation...