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1 |
Cov-Net: A computer-aided diagnosis method for recognizing COVID-19 from chest X-ray images via machine vision
Cov-Net: یک روش تشخیصی به کمک رایانه برای تشخیص COVID-19 از تصاویر اشعه ایکس قفسه سینه از طریق بینایی ماشین-2022 In the context of global pandemic Coronavirus disease 2019 (COVID-19) that threatens life of all human
beings, it is of vital importance to achieve early detection of COVID-19 among symptomatic patients. In this
paper, a computer aided diagnosis (CAD) model Cov-Net is proposed for accurate recognition of COVID-19
from chest X-ray images via machine vision techniques, which mainly concentrates on powerful and robust
feature learning ability. In particular, a modified residual network with asymmetric convolution and attention
mechanism embedded is selected as the backbone of feature extractor, after which skip-connected dilated
convolution with varying dilation rates is applied to achieve sufficient feature fusion among high-level semantic
and low-level detailed information. Experimental results on two public COVID-19 radiography databases have
demonstrated the practicality of proposed Cov-Net in accurate COVID-19 recognition with accuracy of 0.9966
and 0.9901, respectively. Furthermore, within same experimental conditions, proposed Cov-Net outperforms
other six state-of-the-art computer vision algorithms, which validates the superiority and competitiveness of
Cov-Net in building highly discriminative features from the perspective of methodology. Hence, it is deemed
that proposed Cov-Net has a good generalization ability so that it can be applied to other CAD scenarios.
Consequently, one can conclude that this work has both practical value in providing reliable reference to the
radiologist and theoretical significance in developing methods to build robust features with strong presentation
ability.
keywords: COVID-19 | Computer aided diagnosis (CAD) | Feature learning | Image recognition | Machine vision |
مقاله انگلیسی |
2 |
A radiological image analysis framework for early screening of the COVID-19 infection: A computer vision-based approach
چارچوب تجزیه و تحلیل تصویر رادیولوژیکی برای غربالگری اولیه عفونت COVID-19: یک رویکرد مبتنی بر بینایی کامپیوتری-2022 Due to the absence of any specialized drugs, the novel coronavirus disease 2019 or COVID-19 is
one of the biggest threats to mankind Although the RT-PCR test is the gold standard to confirm
the presence of this virus, some radiological investigations find some important features from the
CT scans of the chest region, which are helpful to identify the suspected COVID-19 patients. This
article proposes a novel fuzzy superpixel-based unsupervised clustering approach that can be useful
to automatically process the CT scan images without any manual annotation and helpful in the easy
interpretation. The proposed approach is based on artificial cell swarm optimization and will be
known as the SUFACSO (SUperpixel based Fuzzy Artificial Cell Swarm Optimization) and implemented
in the Matlab environment. The proposed approach uses a novel superpixel computation method
which is helpful to effectively represent the pixel intensity information which is beneficial for the
optimization process. Superpixels are further clustered using the proposed fuzzy artificial cell swarm
optimization approach. So, a twofold contribution can be observed in this work which is helpful
to quickly diagnose the patients in an unsupervised manner so that, the suspected persons can be
isolated at an early phase to combat the spread of the COVID-19 virus and it is the major clinical
impact of this work. Both qualitative and quantitative experimental results show the effectiveness of
the proposed approach and also establish it as an effective computer-aided tool to fight against the
COVID-19 virus. Four well-known cluster validity measures Davies–Bouldin, Dunn, Xie–Beni, and β
index are used to quantify the segmented results and it is observed that the proposed approach not
only performs well but also outperforms some of the standard approaches. On average, the proposed
approach achieves 1.709792, 1.473037, 1.752433, 1.709912 values of the Xie–Beni index for 3, 5,7, and
9 clusters respectively and these values are significantly lesser compared to the other state-of-the-art
approaches. The general direction of this research is worthwhile pursuing leading, eventually, to a
contribution to the community.
keywords: کووید-۱۹ | تفسیر تصویر رادیولوژیکی | سوپرپیکسل | سیستم فازی نوع 2 | بهینه سازی ازدحام سلول های مصنوعی | COVID-19 | Radiological image interpretation | Superpixel | Type 2 fuzzy system | Artificial cell swarm optimization |
مقاله انگلیسی |
3 |
Face mask recogniser using image processing and computer vision approach
تشخیص ماسک صورت با استفاده از پردازش تصویر و رویکرد بینایی کامپیوتری-2022 The world saw a health crisis with the onset of the COVID-19 virus outbreak. The mask has been identified as
the most efficient way to prevent the spread of virus [1]. This has driven the necessity for a face mask recogniser
that not only detects the presence of a mask but also gives the accuracy to which a person is wearing the face
mask. Also, the face mask should be recognised in all angles as well. The goal of this study is to create a new
and improved real time face mask recogniser using image processing and computer vision approach. A Kaggle
dataset which consisted of images with and without masks was used. For the purpose of this study a pre-trained
convolutional neural network Mobile Net V2 was used. The performance of the given model was assessed. The
model presented in this paper can detect the face mask with 98% precision. This Face mask recogniser can effi-
ciently detect the face mask in side wise direction which makes it more useful. A comparison of the performance
metrics of the existing algorithms is also presented. Now with the spread of the infectious variant OMICRON, it
is necessary to implement such a robust face mask recogniser which can help control the spread. keywords: Computer Vision | Convolutional Neural Network | Face mask detection | Image processing | Kaggle dataset | Keras | MobileNetV2 | Open CV | Tensor-Flow |
مقاله انگلیسی |
4 |
آموزش آسیب شناسی از راه دور تحت همه گیری COVID-19: برداشت های دانشجویان پزشکی
سال انتشار: 2022 - تعداد صفحات فایل pdf انگلیسی: 4 - تعداد صفحات فایل doc فارسی: 12 زمینه: همهگیری COVID-19 آموزش سنتی را مجبور کرده است که دوباره ساختار یافته و به صورت آنلاین ارائه شود. هدف: تجزیه و تحلیل ادراک دانشجویان پزشکی در مورد مزایا و مشکلات آموزش از راه دور پاتولوژی در طول همه گیری COVID-19.
طراحی: یک مطالعه مقطعی با یک نظرسنجی آنلاین برای دانشجویان سال سوم و چهارم فارغالتحصیلی پزشکی، که در آموزش از راه دور پاتولوژی در طول همهگیری COVID-19 شرکت کردند، انجام شد. روشهای تدریس آنلاین شامل فعالیتهای همزمان با سخنرانیهای تعاملی زنده، بحثهای مبتنی بر مورد و فعالیتهای ناهمزمان با سخنرانیهای ضبطشده، آموزشها و متون موجود در پلت فرم آموزش آنلاین است. ادراک دانشجویان در مورد آموزش از راه دور آسیب شناسی از طریق نظرسنجی آنلاین مورد ارزیابی قرار گرفت. یافتهها: 90 دانشجو (47%) از 190 شرکتکننده پرسشنامه را تکمیل کردند که 45 نفر مرد و 52 نفر در سال سوم فارغالتحصیلی پزشکی بودند. شرایط درک شده ای که یادگیری آسیب شناسی را تسهیل می کرد شامل استفاده از پلت فرم آموزش آنلاین و انعطاف پذیری زمانی برای مطالعه بود. دانشجویان سخنرانی های زنده تعاملی را برتر از سخنرانی های سنتی سنتی می دانستند. شرایط درک شده ای که مانع اجرای آموزش آنلاین شد، شامل دشواری جداسازی مطالعه از فعالیت های خانگی، بی انگیزگی و بدتر شدن کیفیت زندگی به دلیل دوری فیزیکی از همکاران و اساتید بود. به طور کلی، آموزش از راه دور آسیب شناسی توسط 80٪ از دانشجویان ارزش مثبت داشت. نتیجهگیری: ابزارهای آنلاین اجازه میدهند تا محتوای پاتولوژی با موفقیت در طول همهگیری COVID-19 به دانشآموزان ارائه شود. این تجربه می تواند الگویی برای فعالیت های آموزشی آتی آسیب شناسی در آموزش علوم بهداشت باشد. کلید واژه ها: پاتولوژی | آموزش از راه دور | کووید -19 | آموزش پزشکی |
مقاله ترجمه شده |
5 |
Smart mask – Wearable IoT solution for improved protection and personal health
ماسک هوشمند – راه حل پوشیدنی اینترنت اشیا برای بهبود حفاظت و سلامت شخصی-2022 The use of face masks is an important way to fight the COVID-19 pandemic. In this paper, we
envision the Smart Mask, an IoT supported platform and ecosystem aiming to prevent and control
the spreading of COVID-19 and other respiratory viruses. The integration of sensing, materials,
AI, wireless, IoT, and software will help the gathering of health data and health-related event
detection in real time from the user as well as from their environment. In the larger scale, with the
help of AI-based analysis for health data it is possible to predict and decrease medical costs with
accurate diagnoses and treatment plans, where the comparison of personal data to large-scale
public data enables drawing up a personal health trajectory, for example. Key research prob-
lems for smart respiratory protective equipment are identified in addition to future research di-
rections. A Smart Mask prototype was developed with accompanying user application, backend
and heath AI to study the concept. keywords: کووید-۱۹ | محاسبات لبه | اینترنت اشیا | سلامت شخصی | پوشیدنی | COVID-19 | Edge computing | IoT | Personal health | Wearable |
مقاله انگلیسی |
6 |
“Hey Alexa, what do you know about the COVID-19 vaccine?”— (Mis)perceptions of mass immunization and voice assistants
«هی الکسا، درباره واکسن کووید-19 چه میدانی؟» - برداشتهای نادرست از ایمنسازی انبوه و دستیاران صوتی-2022 In this paper, we analyzed the perceived accuracy of COVID-19 vaccine information spoken
back by Amazon Alexa. Unlike social media, Amazon Alexa does not apply soft moderation to
unverified content, allowing for use of third-party malicious skills to arbitrarily phrase COVID-
19 vaccine information. The results from a 210-participant study suggest that a third-party
malicious skill could successful reduce the perceived accuracy among the users of information as
to who gets the vaccine first, vaccine testing, and the side effects of the vaccine. We also found
that the vaccine-hesitant participants are drawn to pessimistically rephrased Alexa responses
focused on the downsides of the mass immunization. We discuss solutions for soft moderation
against misperception-inducing or other malicious third-party skills.
Keywords: Voice assistants security | Malicious skills | COVID-19 misinformation | IoT security |
مقاله انگلیسی |
7 |
5G network slice for digital real-time healthcare system powered by network data analytics
برش شبکه 5G برای سیستم دیجیتال مراقبت بهداشتی بلادرنگ طراحی شده توسط تجزیه و تحلیل داده های شبکه-2022 In the wake of the COVID-19 pandemic, where almost the entire global healthcare ecosystem struggled to handle
patients, it’s evident that the healthcare segment needs a virtual real-time digital support system. The recent
advancements in technology have enabled machine-to-machine communication, enhanced mobile broadband,
and real-time biometric data analytics. These could potentially fulfill the requirements of an end-to-end digital
healthcare system. For building such a system, there is also a need for a dedicated and specialized communication
network. Such a system will not only support dynamic throughput, latency and payload but also provide guaranteed QoS (Quality of Service) at every instant. The motive of our study was to define an implementable lowlevel architecture for the digital healthcare system by using the 5G Network Slice that incorporates all these
features. Best-in-class wearable devices will collect the biometric data and transmit it via the 5G network slice.
Data analytics is then applied to the collected data to build a knowledge graph used for quick predictions and
prescriptions. The architecture also keeps in mind the security and integrity aspects of healthcare data.
Keywords: 5G network slice | Slice dimensioning | Digital healthcare | Network data analytics framework | IoMT |
مقاله انگلیسی |
8 |
A conceptual IoT-based early-warning architecture for remote monitoring of COVID-19 patients in wards and at home
یک معماری مفهومی هشدار اولیه مبتنی بر اینترنت اشیا برای نظارت از راه دور بیماران COVID-19 در بخش ها و در خانه-2022 Due to the COVID-19 pandemic, health services around the globe are struggling. An effective system for monitoring patients can improve healthcare delivery by avoiding in-person
contacts, enabling early-detection of severe cases, and remotely assessing patients’ status.
Internet of Things (IoT) technologies have been used for monitoring patients’ health with
wireless wearable sensors in different scenarios and medical conditions, such as noncommunicable and infectious diseases. Combining IoT-related technologies with early-warning
scores (EWS) commonly utilized in infirmaries has the potential to enhance health services delivery significantly. Specifically, the NEWS-2 has been showing remarkable results
in detecting the health deterioration of COVID-19 patients. Although the literature presents
several approaches for remote monitoring, none of these studies proposes a customized,
complete, and integrated architecture that uses an effective early-detection mechanism for
COVID-19 and that is flexible enough to be used in hospital wards and at home. Therefore,
this article’s objective is to present a comprehensive IoT-based conceptual architecture that
addresses the key requirements of scalability, interoperability, network dynamics, context
discovery, reliability, and privacy in the context of remote health monitoring of COVID-19
patients in hospitals and at home. Since remote monitoring of patients at home (essential
during a pandemic) can engender trust issues regarding secure and ethical data collection,
a consent management module was incorporated into our architecture to provide transparency and ensure data privacy. Further, the article details mechanisms for supporting a
configurable and adaptable scoring system embedded in wearable devices to increase usefulness and flexibility for health care professions working with EWS.
keywords: نظارت از راه دور | کووید-۱۹ | اخبار-2 | معماری | رضایت | اینترنت اشیا | Remote monitoring | COVID-19 | NEWS-2 | Architecture | Consent | IoT |
مقاله انگلیسی |
9 |
کارآفرینی بینالمللی در دنیای پساکرونا
سال انتشار: 2021 - تعداد صفحات فایل pdf انگلیسی: 7 - تعداد صفحات فایل doc فارسی: 26 دنیای پساکرونا چگونه خواهد بود؟ نقش کارآفرینی بینالمللی (IE) در دنیای جدید چه خواهد بود؟ این مقاله در تلاشش است به دو پرسش پاسخ دهد و بر تغییرات ایجادشده توسط کووید و نحوهی اثرگذاری احتمالی آن بر حوزه و نوع فعالیتهای کارآفرینی بینالمللی در سالیان پیش رو تأکید دارد. این مقاله به بررسی عملکرد احتمالی کارآفرینان و شکلدهی نظم جهانی نوظهور میپردازد. مقالهی پیش رو با تشریح پیامدهای این تغییرات برای کارآفرینی IE، الگویی برای پژوهش آینده ارائه میدهد.
کلیدواژه ها: کارآفرینی بینالمللی | سرمایهگذاریهای جدید بینالمللی (INV ها) | کووید-19 | تغییرات سازمانی | دیجیتالی شدن | نوآوری | الگوی پژوهشی |
مقاله ترجمه شده |
10 |
Evaluation of six commercial SARS-CoV-2 rapid antigen tests in nasopharyngeal swabs: Better knowledge for better patient management?
ارزیابی شش تست آنتی ژن سریع SARS-COV-2 در سواب های نازوفارنکس: دانش بهتر برای مدیریت بهتر بیمار؟-2021 Robust antigen point-of-care SARS-CoV-2 tests have been proposed as an efficient tool to address the COVID-19
pandemic. This requirement was raised after acknowledging the constraints that are brought by molecular
biology. However, worldwide markets have been flooded with cheap and potentially underperforming lateral
flow assays. Herein we retrospectively compared the overall performance of five qualitative rapid antigen SARS-
CoV-2 assays and one quantitative automated test on 239 clinical swabs. While the overall sensitivity and
specificity are relatively similar for all tests, concordance with molecular based methods varies, ranging from
75,7% to 83,3% among evaluated tests. Sensitivity is greatly improved when considering patients with higher
viral excretion (Ct≤33), proving that antigen tests accurately distinguish infectious patients from viral shedding.
These results should be taken into consideration by clinicians involved in patient triage and management, as well
as by national authorities in public health strategies and for mass campaign approaches. keywords: SARS-DONE-2 | تست های آنتی ژن سریع | rt-pcr | کووید -19 | SARS-CoV-2 | Rapid antigen tests | RT-PCR | COVID-19 |
مقاله انگلیسی |