Comparison Between ResNet 16 and Inception V4 Network for COVID-19 Prediction

COVID-19 claimed 5 million lives worldwide so far, and the count is continuing. It also affected socio-economic life of almost everybody in the world. Due to COVID-19, mortality and morbidity are continuing, and it is necessary to find new methods and techniques to contain the infection. Every government is trying hard to implement a new strategy to minimize the spread of the virus. COVID-19 infection occurs due to the virus strain SARS-COV-2. Generally, death occurs due to COVID-19 because of suppurative pulmonary infection and subsequent septic shock or multiorgan failure. In the literature, there are some computational techniques which use deep learning models and reported fairly good performance. This paper proposes a new deep learning architecture inception v4 to automatically detect COVID-19 using the chart X-ray images. The proposed methodology provided improved performance of 98.7 and 94.8% of training and validation accuracy. The developed technology can be used to detect COVID-19 with a high performance;the same may be deployed by the various governments in the detection and the management of COVID-19 in an efficient manner. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Covid Vaccine Adverse Side-Effects Prediction with Sequence-to-Sequence Model

COVID-19 caused more than 5 million deaths in the world. After lot of efforts and hard work of many scientists, few vaccines are discovered and are approved for use. It is necessary to understand and to evaluate systematically with the potential side effects due to the vaccine itself. This work proposed a sequence-to-sequence learning (Seq2Seq) model to predict the adverse effects due to COVID-19 vaccine. Seq2Seq model is used to convert sequences of one domain to another domain. In this work, a structured data such as Vaccine Adverse Event Reposting System (VAERS) data are used to predict the adverse side effects of COVID-19 vaccination. The data formulated for Seq2Seq model architecture and trying to predict the adverse side effects of vaccination with age and gender attribute as input and obtained the result of 88% as average accuracy using long short-term memory-based (LSTM) deep learning model in adverse effect prediction. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Application of Continuous Embedding of Viral Genome Sequences and Machine Learning in the Prediction of SARS-CoV-2 Variants

Since the beginning of the novel coronavirus pandemic, Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has spread to 224 countries with over 430 million confirmed cases and more than 5,97 million deaths worldwide. One of the crucial reasons why the spread of the virus was difficult to stop was the viral evolution over time. The emergence of new virus variants is hindering the development of effective drugs and vaccines. Moreover, they contribute e.g. to virus transmissibility or viral immune evasion. This fact has led to increased importance of understanding genomic data related to SARS-CoV-2. In this study, we are proposing sarscov2vec, a new application of continuous vector space representation on novel species of coronaviruses genomes. With its core methodology of genome feature extraction step and being supervised by a Machine Learning model, this tool is designed to distinguish the most common five different SARS-CoV-2 variants: Alpha, Beta, Delta, Gamma and Omicron. In this research we used 367,004 unique genome sequence records from the official virus repositories, where 25,000 sequences were randomly selected and used to train the Natural Language Processing (NLP) algorithm. The next 36,365 samples were processed by a Machine Learning pipeline. Our research results show that the final hiper-tuned classification model achieved 99% of accuracy on the test set. Furthermore, this study demonstrated that the continuous vector space representation of SARS-CoV-2 genomes can be decomposed into 2D vector space and visualized as a method of explaining Machine Learning model decisions. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Natural Language Interface for Covid-19 Amharic Database Using LSTM Encoder Decoder Architecture with Attention

The COVID-19 outbreak is still a challenge in most places because of lack of up-to-date information, primarily, to the people in the world who speak and use underrepresented local languages. Ethiopia is one example of a country where several in-digenous languages are under-represented and under-resourced. Thus, building an interactive interface that responds to users' query using their local language with organized information plays a significant role. In this study, attention-augmented Encoder-Decoder Long Short Term Memory(LSTM) network model has proposed to provide adequate information about the pandemic to the people of Ethiopia by their local language, Amharic. The model converts Amharic COVID-19 related questions into the corresponding structured query language (SQL). The model retrieves information from the Amharic COVID-19 database that has developed for this study. The database contains frequently referenced COVID-19 attributes such as symptoms, prevention, transmission and frequently asked questions. In addition, a parallel Amharic Question-SQL query dataset has been prepared to evaluate the model. The LSTM Network with augmented attention mechanism has shown a clear significant result. In this study, a user interactive interface has also developed. The interface uses the proposed model and provides information about the pandemic to the people with questions in Amharic. © 2021 IEEE.

MedSeq2Seq: A Medical Knowledge Enriched Sequence to Sequence Learning Model for COVID-19 Diagnosis

The COVID-19 pandemic has had a severe impact on humans' lives and and healthcare systems worldwide. How to early, fastly and accurately diagnose infected patients via multimodal learning is now a research focus. The central challenges in this task mainly lie on multi-modal data representation and multi-modal feature fusion. To solve such challenges, we propose a medical knowledge enriched multi-modal sequence to sequence learning model, termed MedSeq2Seq. The key components include two attention mechanisms, viz. intra-modal (Ia) and inter-model (Ie) attentions, and a medical knowledge augmentation mechanism. The former two mechanisms are to learn multi-modal refined representation, while the latter aims to incorporate external medical knowledge into the proposed model. The experimental results show the effectiveness of the proposed MedSeq2Seq framework over state-of-the-art baselines with a significant improvement of 1%-2%. © 2021 IEEE.

Disease Progression Detection via Deep Sequence Learning of Successive Radiographic Scans.

The highly rapid spread of the current pandemic has quickly overwhelmed hospitals all over the world and motivated extensive research to address a wide range of emerging problems. The unforeseen influx of COVID-19 patients to hospitals has made it inevitable to deploy a rapid and accurate triage system, monitor progression, and predict patients at higher risk of deterioration in order to make informed decisions regarding hospital resource management. Disease detection in radiographic scans, severity estimation, and progression and prognosis prediction have been extensively studied with the help of end-to-end methods based on deep learning. The majority of recent works have utilized a single scan to determine severity or predict progression of the disease. In this paper, we present a method based on deep sequence learning to predict improvement or deterioration in successive chest X-ray scans and build a mathematical model to determine individual patient disease progression profile using successive scans. A deep convolutional neural network pretrained on a diverse lung disease dataset was used as a feature extractor to generate the sequences. We devised three strategies for sequence modeling in order to obtain both fine-grained and coarse-grained features and construct sequences of different lengths. We also devised a strategy to quantify positive or negative change in successive scans, which was then combined with age-related risk factors to construct disease progression profile for COVID-19 patients. The age-related risk factors allowed us to model rapid deterioration and slower recovery in older patients. Experiments conducted on two large datasets showed that the proposed method could accurately predict disease progression. With the best feature extractor, the proposed method was able to achieve AUC of 0.98 with the features obtained from radiographs. Furthermore, the proposed patient profiling method accurately estimated the health profile of patients.

A deep learning framework using CNN and stacked Bi-GRU for COVID-19 predictions in India.

The novel coronavirus infection (COVID-19) first appeared in Wuhan, China, in December 2019. COVID-19 declared as a global pandemic by the WHO was the most rapidly spreading disease all across the world. India, the second most populated nation in the world, is still fighting it, when coronavirus reached the stage where community transmission takes place at an exponential rate. Therefore, it is crucial to examine the future trends of COVID-19 in India and anticipate how it will affect economic and social growth in a short run. In this paper, a new deep learning framework using CNN and stacked Bi-GRU has been developed for predicting and analyzing the COVID-19 cases in India. The proposed model can predict the next 30 days' new positive cases, new death cases, recovery rate and containment and health index values with high accuracy. The proposed method is compared against Gaussian process regression (GPR) model on COVID-19 datasets. The experimental result shows that the proposed framework is highly reliable for COVID-19 prediction over the GPR model.