Insights on the methodology for indoor microbial aerosol studies

Covid-19 has highlighted the need for reliable methods for airborne microbe control. Different microbes are suitable for different purposes, and the microbes are sensitive to collection methods used. We identified three safe-to-use microbes suitable for airborne microbial studies: MS2-bacteriophage virus, Staphylococcus simulans and Bacillus atrophaeus bacterial spores. We found that the sensitive microbes (MS2 and S. simulans) survive better, when collected directly in a liquid media. © 2022 17th International Conference on Indoor Air Quality and Climate, INDOOR AIR 2022. All rights reserved.

Detection of COVID-19 from X-RAY Images using Artificial Intelligence (AI)

Corona virus (COVID-19) is an infectious disease. Several millions of people worldwide suffer from this disease. The signs of progress of virus infection are more severe damage to lungs and causes to organs failure, death. X-rays are readily available and an excellent alternative method to x-ray imaging in the diagnosis of covid-19 and very crucial role play to recognizing this disease and recovery with hospitalization. The goal of this revise is to expand a reliable method for detecting COVID-19 from digital chest X-ray pictures using well-before deep-learning algorithms while optimizing detection performance. To train and verify, the transfer learning (TL) approach was utilized with the aid of picture extension. Current would be hugely beneficial in this pandemic because the illness severity and the necessity for prevention methods are at odds with available resources. © 2022 IEEE.

Covid-19 Detection Based on the Fine-Tuned MobileNetv2 Through Lung X-rays

Covid-19 is has become an epidemic, which is affecting millions of people around the world. The common symptoms of Covid-19 are cough and fever, which are very similar to the normal Flu. Covid-19 spreads fast and is devastating for people of all ages especially elderly and people having weak immune system. The standard technique used for Covid-19 detection is real-time polymerase chain reaction (RT-PCR) test. However, RT-PCR is unreliable for Covid-19 detection as it takes long time to detect the disease and it produces considerable number of false positive cases. Therefore, we need to propose an automated and reliable method for Covid-19 detection. Radiographic images are widely used for the detection of various pulmonary diseases such as lung cancer, asthma, pneumonia, etc. We also used chest x-rays for the diagnosis of Covid-19. In this paper, we employed two deep learning models such as SqueezeNet and MobileNetv2 and fine-tuned to check the classification performance. Moreover, we performed data augmentation technique to increase the amount of data and avoid the overfitting of model. We evaluated the performance of the proposed system on standard dataset Covid-19 Radiography dataset that is publicly available. More specifically, we achieved remarkable accuracy of 97%, precision of 95.19%, recall of 100%, specificity of 95%, area under the curve of 98.93%, and F1-score of 97.06% on MobileNetv2. Experimental results and comparative analysis with other existing methods demonstrate that our method is reliable than PT-PCR and other existing state-of-the-art methods for Covid-19 detection. © 2021 IEEE.

Transfer learning approaches to recognize X-ray Covid-19 images

Covid 19, caused from coronavirus SAR-CoV-2, is currently a dangerous threat to human beings. The rapid development of the Covid 19 pandemic forced all countries to develop fast and reliable methods to detect the coronavirus SAR-CoV-2. Transfer learning with medical images is a suitable such detecting method. Transfer learning, a deep learning technique, has special abilities such as speed of training, fewer requirements of training data set size and reduced demand of expert domain knowledge. Diagnosing Covid 19 using medical images is also considered by some to be more reliable than using traditional laboratory methods. This paper proposes transfer learning methods combined with medical images to detect Covid 19. Using a Covid 19 X-ray data set from Kaggle, this research considers viral pneumonia as a separate class, increasing the performance since viral pneumonia is often wrongly classified as Covid 19, even by radiologists. This paper uses specialized metrics to deal with the imbalanced nature of the data and visualises results using Local Interpretable Model-agnostic Explanations to indicate areas of images associated with Covid 19. The ResNet family of CNNs performed well, with ResNet 34 performing better than the 18 and 50 layer versions. Inception and DenseNet also have good classification performance. © 2021 IEEE.

Fusion of image quality assessment and transfer learning for COVID19 detection using CT scan image

One of the main challenges in controlling the spread of COVID19 pandemic is to diagnose infection early. The most reliable method RT - PCR takes several hours to give results. Although the Anti-Body (Serological) test gives the results in a few hours, it is not accurate, and hence it is not reliable. Moreover, they are invasive. Another issue with these methods is that the number of labs performing these tests are very limited. It will be beneficial if the already existing clinical infrastructure is used for diagnosing COVID19 accurately in real time. Recently chest CT images are used by researchers to diagnose the COVID19 with impressive accuracy. The state of the art method for detecting COVID19 using CT chest images involves Deep Learning. Deep Learning is expected to provide accurate and reliable results only when the model is trained on a large data set. Due to non-availability of a large data set the existing models have been trained on a smaller size data set. Therefore it would be better to design a model to give good accuracy with reliability. To achieve accuracy along with reliability we proposed a COVID19 detection model with the combination of deep learning model and the traditional machine learning model. The novelty of the proposed model is the fusion of image quality and deep learning. The proposed method outperformed the state of the art method in terms of accuracy, recall and F1 score (more than 99 % in almost all the metrics) on a benchmark data set. The efficacy of the selected features and also explainability of the method are demonstrated through various tests. © 2021 ACM.