• Korean Journal of Radiology
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• v.21 no.10
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• pp.1161-1164
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• 2020

The epidemic of 2019 novel coronavirus, later named as coronavirus disease (COVID-19), began in Wuhan, China in December 2019 and has spread rapidly worldwide. Early diagnosis is crucial for the management of the patients with COVID-19, but the gold standard diagnostic test for this infection, the reverse transcriptase polymerase chain reaction, has a low sensitivity and an increased turnaround time. In this scenario, chest computed tomography (CT) could play a key role for an early diagnosis of COVID-19 pneumonia. Here, we have reported a confirmed case of COVID-19 with an atypical CT presentation showing a "double halo sign," which we believe represents the pathological spectrum of this viral pneumonia.

• International Journal of Computer Science & Network Security
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• v.21 no.3
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• pp.83-93
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• 2021

COVID-19 poses a major risk to global health, highlighting the importance of faster and proper diagnosis. To handle the rise in the number of patients and eliminate redundant tests, healthcare information exchange and medical data are transmitted between healthcare centres. Medical data sharing helps speed up patient treatment; consequently, exchanging healthcare data is the requirement of the present era. Since healthcare professionals share data through the internet, security remains a critical challenge, which needs to be addressed. During the COVID-19 pandemic, computed tomography (CT) and X-ray images play a vital part in the diagnosis process, constituting information that needs to be shared among hospitals. Encryption and image steganography techniques can be employed to achieve secure data transmission of COVID-19 images. This study presents a new encryption with the image steganography model for secure data transmission (EIS-SDT) for COVID-19 diagnosis. The EIS-SDT model uses a multilevel discrete wavelet transform for image decomposition and Manta Ray Foraging Optimization algorithm for optimal pixel selection. The EIS-SDT method uses a double logistic chaotic map (DLCM) is employed for secret image encryption. The application of the DLCM-based encryption procedure provides an additional level of security to the image steganography technique. An extensive simulation results analysis ensures the effective performance of the EIS-SDT model and the results are investigated under several evaluation parameters. The outcome indicates that the EIS-SDT model has outperformed the existing methods considerably.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.11a
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• pp.823-826
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• 2021

The recent COVID-19 pandemic has accentuated the need for faster and more accurate ways of diagnosing certain diseases for there to be safer and more effective early responses that help to prevent a total outbreak. In this work, we would like to approach this issue through machine learning algorithms to investigate whether or not they could serve as a viable replacement for conventional diagnosis. Through a process of training and testing various algorithms, we analyzed how successfully they can predict a patient's COVID-19 diagnosis based on a list of symptoms and also identified which algorithm is the most effective at doing so. If the necessary data, containing the symptoms and diagnoses of different cases, is provided, this method can be utilized to make a probable diagnosis of any disease besides COVID-19. This method can be used in conjunction with or in lieu of conventional diagnosis depending on the situation: if there is a lack of testing facilities or test kits, this method can be employed as it is inexhaustible and it could also be used in situations where a conventional diagnosis is proven to be inaccurate.

• The Journal of the Acoustical Society of Korea
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• v.42 no.3
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• pp.221-225
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• 2023

In this paper, we proposed an efficient method for rapid diagnosis of COVID-19 by voice. A novel Strided Convolution Separable Transformer (SC-SepTr) is proposed by modifying the conventional Separable Transformer (SepTr) for audio signal recognition. The proposed method reduces the memory and computational requirements to enable rapid diagnosis of COVID-19. As a result of experiments on Coswara, it was shown that the proposed method perform rapid diagnosis with guaranteeing Area Under the Curve (AUC) performance even for a relatively small amount of learning data.

• International Journal of Computer Science & Network Security
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• v.24 no.3
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• pp.125-134
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• 2024

The novel coronavirus 2019 is called COVID-19 has outspread swiftly worldwide. An early diagnosis is more important to control its quick spread. Medical imaging mechanics, chest calculated tomography or chest X-ray, are playing a vital character in the identification and testing of COVID-19 in this present epidemic. Chest X-ray is cost effective method for Covid-19 detection however the manual process of x-ray analysis is time consuming given that the number of infected individuals keep growing rapidly. For this reason, it is very important to develop an automated COVID-19 detection process to control this pandemic. In this study, we address the task of automatic detection of Covid-19 by using a popular deep learning model namely the VGG19 model. We used 1300 healthy and 1300 confirmed COVID-19 chest X-ray images in this experiment. We performed three experiments by freezing different blocks and layers of VGG19 and finally, we used a machine learning classifier SVM for detecting COVID-19. In every experiment, we used a five-fold cross-validation method to train and validated the model and finally achieved 98.1% overall classification accuracy. Experimental results show that our proposed method using the deep learning-based VGG19 model can be used as a tool to aid radiologists and play a crucial role in the timely diagnosis of Covid-19.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.15 no.2
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• pp.117-123
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• 2022

Currently the number of COVID-19 cases is increasing rapidly around the world. One way to restrict the spread of COVID-19 infection is to find confirmed cases using rapid diagnosis. The previously proposed group testing problem assumed without measurement noise, but recently, false positive and false negative cases have occurred during COVID-19 testing. In this paper, we define the noisy group testing problem and analyze how much measurement noise affects the performance. In this paper, we show that the group testing system should be designed to be less susceptible to measurement noise when conducting group testing with a low positive rate of COVID-19 infection. And compared with other developed reconstruction algorithms, our proposed algorithm shows superior performance in noisy group testing.

• Journal of Gastric Cancer
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• v.23 no.4
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• pp.574-583
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• 2023

Purpose: Gastric cancer (GC) is among the most prevalent and fatal cancers worldwide. National cancer screening programs in countries with high incidences of this disease provide medical aid beneficiaries with free-of-charge screening involving upper endoscopy to detect early-stage GC. However, the coronavirus disease 2019 (COVID-19) pandemic has caused major disruptions to routine healthcare access. Thus, this study aimed to assess the impact of COVID-19 on the diagnosis, overall incidence, and stage distribution of GC. Materials and Methods: We identified patients in our hospital cancer registry who were diagnosed with GC between January 2018 and December 2021 and compared the cancer stage at diagnosis before and during the COVID-19 pandemic. Subgroup analyses were conducted according to age and sex. The years 2018 and 2019 were defined as the "before COVID" period, and the years 2020 and 2021 as the "during COVID" period. Results: Overall, 10,875 patients were evaluated; 6,535 and 4,340 patients were diagnosed before and during the COVID-19 period, respectively. The number of diagnoses was lower during the COVID-19 pandemic (189 patients/month vs. 264 patients/month) than before it. Notably, the proportion of patients with stages 3 or 4 GC in 2021 was higher among men and patients aged ≥40 years. Conclusions: During the COVID-19 pandemic, the overall number of GC diagnoses decreased significantly in a single institute. Moreover, GCs were in more advanced stages at the time of diagnosis. Further studies are required to elucidate the relationship between the COVID-19 pandemic and the delay in the detection of GC worldwide.

• Journal of the Korean Society of Laryngology, Phoniatrics and Logopedics
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• v.33 no.3
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• pp.183-187
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• 2022

Dysphonia is well known as one of the otolaryngological symptoms of coronavirus disease 2019 (COVID-19) infection. The vocal changes of the COVID-19 condition have been reported in terms of parameters of multi-dimensional voice assessment, including acoustic analysis, auditory-perceptual evaluation, and psychometric assessment. However, there has not been a daily followup study in patients with acute COVID-19 infection. In this study, a 41-year-old male performed daily voice recordings of vowel phonation and passage-reading tasks during the self-quarantine period of one week. Compared to the normal voice status of the prepandemic period, voice abnormalities peaked on day two after the diagnosis of COVID-19 infection and recovered after one week.

• Journal of Microbiology and Biotechnology
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• v.30 no.3
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• pp.313-324
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• 2020

Coronavirus disease 2019 (COVID-19), which causes serious respiratory illness such as pneumonia and lung failure, was first reported in Wuhan, the capital of Hubei, China. The etiological agent of COVID-19 has been confirmed as a novel coronavirus, now known as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which is most likely originated from zoonotic coronaviruses, like SARS-CoV, which emerged in 2002. Within a few months of the first report, SARS-CoV-2 had spread across China and worldwide, reaching a pandemic level. As COVID-19 has triggered enormous human casualties and serious economic loss posing global threat, an understanding of the ongoing situation and the development of strategies to contain the virus's spread are urgently needed. Currently, various diagnostic kits to test for COVID-19 are available and several repurposing therapeutics for COVID-19 have shown to be clinically effective. In addition, global institutions and companies have begun to develop vaccines for the prevention of COVID-19. Here, we review the current status of epidemiology, diagnosis, treatment, and vaccine development for COVID-19.

• International Journal of Computer Science & Network Security
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• v.24 no.3
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• pp.107-112
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• 2024

Nowadays the deadly virus famous as COVID-19 spread all over the world starts from the Wuhan China in 2019. This disease COVID-19 Virus effect millions of people in very short time. There are so many symptoms of COVID19 perhaps the Identification of a person infected with COVID-19 virus is really a difficult task. Moreover it's a challenging task to identify whether a person or individual have covid test positive or negative. We are developing a framework in which we used machine learning techniques..The proposed method uses DecisionTree, KNearestNeighbors, GaussianNB, LogisticRegression, BernoulliNB , RandomForest , Machine Learning methods as the classifier for diagnosis of covid ,however, 5-fold and 10-fold cross-validations were applied through the classification process. The experimental results showed that the best accuracy obtained from Decision Tree classifiers. The data preprocessing techniques have been applied for improving the classification performance. Recall, accuracy, precision, and F-score metrics were used to evaluate the classification performance. In future we will improve model accuracy more than we achieved now that is 93 percent by applying different techniques