A Lightweight Depthwise Separable Convolution Neural Network for Screening Covid-19 Infection from Chest CT and X-ray Images

Because Covid-19 spreads swiftly in the community, an automatic detection system is required to prevent Covid-19 from spreading among humans as a rapid diagnostic tool. In this paper, we propose to employ Convolution Neural Networks to detect coronavirus-infected patients using Computed Tomography and X-ray images. In addition, we look into the transfer learning of a deep CNN model, DenseNet201 for detecting infection from CT and X-ray scans. Grid Search optimization is utilized to select ideal values for hyperparameters, while image augmentation is employed to increase the model's capacity to generalize. We further modify DenseNet architecture to incorporate a depthwise separable convolution for detecting coronavirus-infected patients utilizing CT and Xray images. Interestingly, all of the proposed models scored greater than 94% accuracy, which is equivalent to or higher than the accuracy of earlier deep learning models. Further, we demonstrate that depthwise separable convolution reduces the training time and computation complexity.

Screening Covid-19 Infection from Chest CT Images using Deep Learning Models based on Transfer Learning

As the global epidemic of Covid19 progresses, accurate diagnosis of Covid19 patients becomes important. The biggest problem in diagnosing test-positive people is the lack or lack of test kits due to the rapid spread of Covid19 in the community. As an alternative rapid diagnostic method, an automated detection system is needed to prevent Covid 19 from spreading to humans. This article proposes to use a convolutional neural network (CNN) to detect patients infected with coronavirus using computer tomography (CT) images. In addition, the transfer learning of the deep CNN model VGG16 is investigated to detect infections on CT scans. The pretrained VGG16 classifier is used as a classifier, feature extractor, and fine tuner in three different sets of tests. Image augmentation is used to boost the model's generalization capacity, while Bayesian optimization is used to pick optimum values for hyperparameters. In order to fine-tune the models and reduce training time, transfer learning is being researched. Surprisingly, all of the proposed models scored greater than 93% accuracy, which is on par with or better than previous deep learning models. The results show that optimization improved generalization in all models and highlight the efficacy of the proposed strategies. © 2022 IEEE.

Allergy/Immunology Trainee Experiences During the COVID-19 Pandemic: AAAAI Work Group Report of the Fellows-in-Training Committee.

As a result of the coronavirus disease 2019 (COVID-19) global pandemic, medical trainees have faced unique challenges and uncertainties. To capture the experiences of allergy and immunology fellows throughout the United States and Canada during this time, a 17-item electronic questionnaire was distributed to 380 fellow-in-training (FIT) members of the American Academy of Allergy, Asthma, and Immunology enrolled in US and Canadian allergy/immunology fellowship programs. Voluntary and anonymous responses were collected from April 15 to May 15, 2020. In addition to summary statistics, categorical data were compared using χ2 tests (Fisher's exact). Responses were obtained from FITs across all years of training and primary specialties (Internal Medicine, Pediatrics, and Medicine-Pediatrics) with a response rate of 32.6% (124 of 380). Reassignment to COVID-19 clinical responsibilities was reported by 12% (15 of 124) of FITs, with the largest proportion in the US northeast region. A majority of FITs used telehealth (95%) and virtual learning (82%) during the pandemic. Overall, 21% (25 of 120) of FITs expressed concern about potentially lacking clinical experience for independently practicing allergy and immunology. However, FITs using telehealth reported lower concern compared with those who did not (18.4% [21 of 114] vs 66.7% [4 of 6]; P = .01). The survey shows that allergy and immunology trainee experiences have varied considerably since the COVID-19 outbreak. Notably, the adoption of telehealth and virtual learning was commonly reported, and optimization of these virtual experiences will be helpful. Even outside of pandemics, training on the use of telemedicine may be a sound strategy in preparation for future health care delivery and unexpected events.

Asthma among hospitalized patients with COVID-19 and related outcomes.

BACKGROUND: Several underlying conditions have been associated with severe acute respiratory syndrome coronavirus 2 illness, but it remains unclear whether underlying asthma is associated with worse coronavirus disease 2019 (COVID-19) outcomes. OBJECTIVE: Given the high prevalence of asthma in the New York City area, our objective was to determine whether underlying asthma was associated with poor outcomes among hospitalized patients with severe COVID-19 compared with patients without asthma. METHODS: Electronic heath records were reviewed for 1298 sequential patients 65 years or younger without chronic obstructive pulmonary disease who were admitted to our hospital system with a confirmed positive severe acute respiratory syndrome coronavirus 2 test result. RESULTS: The overall prevalence of asthma among all hospitalized patients with COVID-19 was 12.6%, yet a higher prevalence (23.6%) was observed in the subset of 55 patients younger than 21 years. There was no significant difference in hospital length of stay, need for intubation, length of intubation, tracheostomy tube placement, hospital readmission, or mortality between patients with and without asthma. Observations between patients with and without asthma were similar when stratified by obesity, other comorbid conditions (ie, hypertension, hyperlipidemia, and diabetes), use of controller asthma medication, and absolute eosinophil count. CONCLUSIONS: Among hospitalized patients 65 years or younger with severe COVID-19, asthma diagnosis was not associated with worse outcomes, regardless of age, obesity, or other high-risk comorbidities. Future population-based studies are needed to investigate the risk of developing COVID-19 among patients with asthma once universal testing becomes readily available.