A lightweight network for COVID-19 detection in X-ray images.

The Novel Coronavirus 2019 (COVID-19) is a global pandemic which has a devastating impact. Due to its quick transmission, a prominent challenge in confronting this pandemic is the rapid diagnosis. Currently, the commonly-used diagnosis is the specific molecular tests aided with the medical imaging modalities such as chest X-ray (CXR). However, with the large demand, the diagnoses of CXR are time-consuming and laborious. Deep learning is promising for automatically diagnosing COVID-19 to ease the burden on medical systems. At present, the most applied neural networks are large, which hardly satisfy the rapid yet inexpensive requirements of COVID-19 detection. To reduce huge computation and memory demands, in this paper, we focus on implementing lightweight networks for COVID-19 detection in CXR. Concretely, we first augment data based on clinical visual features of CXR from expertise. Then, according to the fact that all the input data are CXR, we design a targeted four-layer network with either 11 × 11 or 3 × 3 kernels to recognize regional features and detail features. A pruning criterion based on the weights importance is also proposed to further prune the network. Experiments on a public COVID-19 dataset validate the effectiveness and efficiency of the proposed method.

Microfluidic-based technologies for diagnosis, prevention, and treatment of COVID-19: recent advances and future directions.

The COVID-19 pandemic has posed significant challenges to existing healthcare systems around the world. The urgent need for the development of diagnostic and therapeutic strategies for COVID-19 has boomed the demand for new technologies that can improve current healthcare approaches, moving towards more advanced, digitalized, personalized, and patient-oriented systems. Microfluidic-based technologies involve the miniaturization of large-scale devices and laboratory-based procedures, enabling complex chemical and biological operations that are conventionally performed at the macro-scale to be carried out on the microscale or less. The advantages microfluidic systems offer such as rapid, low-cost, accurate, and on-site solutions make these tools extremely useful and effective in the fight against COVID-19. In particular, microfluidic-assisted systems are of great interest in different COVID-19-related domains, varying from direct and indirect detection of COVID-19 infections to drug and vaccine discovery and their targeted delivery. Here, we review recent advances in the use of microfluidic platforms to diagnose, treat or prevent COVID-19. We start by summarizing recent microfluidic-based diagnostic solutions applicable to COVID-19. We then highlight the key roles microfluidics play in developing COVID-19 vaccines and testing how vaccine candidates perform, with a focus on RNA-delivery technologies and nano-carriers. Next, microfluidic-based efforts devoted to assessing the efficacy of potential COVID-19 drugs, either repurposed or new, and their targeted delivery to infected sites are summarized. We conclude by providing future perspectives and research directions that are critical to effectively prevent or respond to future pandemics.

TVFx - CoVID-19 X-Ray images classification approach using neural networks based feature thresholding technique.

COVID-19, the global pandemic of twenty-first century, has caused major challenges and setbacks for researchers and medical infrastructure worldwide. The CoVID-19 influences on the patients respiratory system cause flooding of airways in the lungs. Multiple techniques have been proposed since the outbreak each of which is interdepended on features and larger training datasets. It is challenging scenario to consolidate larger datasets for accurate and reliable decision support. This research article proposes a chest X-Ray images classification approach based on feature thresholding in categorizing the CoVID-19 samples. The proposed approach uses the threshold value-based Feature Extraction (TVFx) technique and has been validated on 661-CoVID-19 X-Ray datasets in providing decision support for medical experts. The model has three layers of training datasets to attain a sequential pattern based on various learning features. The aligned feature-set of the proposed technique has successfully categorized CoVID-19 active samples into mild, serious, and extreme categories as per medical standards. The proposed technique has achieved an accuracy of 97.42% in categorizing and classifying given samples sets.

Evaluating Listening Performance for COVID-19 Detection by Clinicians and Machine Learning: Comparative Study.

BACKGROUND: To date, performance comparisons between men and machines have been carried out in many health domains. Yet machine learning (ML) models and human performance comparisons in audio-based respiratory diagnosis remain largely unexplored. OBJECTIVE: The primary objective of this study was to compare human clinicians and an ML model in predicting COVID-19 from respiratory sound recordings. METHODS: In this study, we compared human clinicians and an ML model in predicting COVID-19 from respiratory sound recordings. Prediction performance on 24 audio samples (12 tested positive) made by 36 clinicians with experience in treating COVID-19 or other respiratory illnesses was compared with predictions made by an ML model trained on 1162 samples. Each sample consisted of voice, cough, and breathing sound recordings from 1 subject, and the length of each sample was around 20 seconds. We also investigated whether combining the predictions of the model and human experts could further enhance the performance in terms of both accuracy and confidence. RESULTS: The ML model outperformed the clinicians, yielding a sensitivity of 0.75 and a specificity of 0.83, whereas the best performance achieved by the clinicians was 0.67 in terms of sensitivity and 0.75 in terms of specificity. Integrating the clinicians' and the model's predictions, however, could enhance performance further, achieving a sensitivity of 0.83 and a specificity of 0.92. CONCLUSIONS: Our findings suggest that the clinicians and the ML model could make better clinical decisions via a cooperative approach and achieve higher confidence in audio-based respiratory diagnosis.

Visual transformer and deep CNN prediction of high-risk COVID-19 infected patients using fusion of CT images and clinical data.

BACKGROUND: Despite the globally reducing hospitalization rates and the much lower risks of Covid-19 mortality, accurate diagnosis of the infection stage and prediction of outcomes are clinically of interest. Advanced current technology can facilitate automating the process and help identifying those who are at higher risks of developing severe illness. This work explores and represents deep-learning-based schemes for predicting clinical outcomes in Covid-19 infected patients, using Visual Transformer and Convolutional Neural Networks (CNNs), fed with 3D data fusion of CT scan images and patients' clinical data. METHODS: We report on the efficiency of Video Swin Transformers and several CNN models fed with fusion datasets and CT scans only vs. a set of conventional classifiers fed with patients' clinical data only. A relatively large clinical dataset from 380 Covid-19 diagnosed patients was used to train/test the models. RESULTS: Results show that the 3D Video Swin Transformers fed with the fusion datasets of 64 sectional CT scans + 67 clinical labels outperformed all other approaches for predicting outcomes in Covid-19-infected patients amongst all techniques (i.e., TPR = 0.95, FPR = 0.40, F0.5 score = 0.82, AUC = 0.77, Kappa = 0.6). CONCLUSIONS: We demonstrate how the utility of our proposed novel 3D data fusion approach through concatenating CT scan images with patients' clinical data can remarkably improve the performance of the models in predicting Covid-19 infection outcomes. SIGNIFICANCE: Findings indicate possibilities of predicting the severity of outcome using patients' CT images and clinical data collected at the time of admission to hospital.

Lightweight deep CNN-based models for early detection of COVID-19 patients from chest X-ray images.

Hundreds of millions of people worldwide have recently been infected by the novel Coronavirus disease (COVID-19), causing significant damage to the health, economy, and welfare of the world's population. Moreover, the unprecedented number of patients with COVID-19 has placed a massive burden on healthcare centers, making timely and rapid diagnosis challenging. A crucial step in minimizing the impact of such problems is to automatically detect infected patients and place them under special care as quickly as possible. Deep learning algorithms, such as Convolutional Neural Networks (CNN), can be used to meet this need. Despite the desired results, most of the existing deep learning-based models were built on millions of parameters (weights), which are not applicable to devices with limited resources. Inspired by such fact, in this research, we developed two new lightweight CNN-based diagnostic models for the automatic and early detection of COVID-19 subjects from chest X-ray images. The first model was built for binary classification (COVID-19 and Normal), whereas the second one was built for multiclass classification (COVID-19, viral pneumonia, or normal). The proposed models were tested on a relatively large dataset of chest X-ray images, and the results showed that the accuracy rates of the 2- and 3-class-based classification models are 98.55% and 96.83%, respectively. The results also revealed that our models achieved competitive performance compared with the existing heavyweight models while significantly reducing cost and memory requirements for computing resources. With these findings, we can indicate that our models are helpful to clinicians in making insightful diagnoses of COVID-19 and are potentially easily deployable on devices with limited computational power and resources.

MALDI-TOF Mass Spectrometric Detection of SARS-CoV-2 Using Cellulose Sulfate Ester Enrichment and Hot Acid Treatment.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes the ongoing coronavirus disease 2019 (COVID-19) pandemic. Here we report a novel strategy for the rapid detection of SARS-CoV-2 based on an enrichment approach exploiting the affinity between the virus and cellulose sulfate ester functional groups, hot acid hydrolysis, and matrix-assisted laser desorption/ionization-time-of-flight mass spectrometry (MALDI-TOF MS). Virus samples were enriched using cellulose sulfate ester microcolumns. Virus peptides were prepared using the hot acid aspartate-selective hydrolysis and characterized by MALDI-TOF MS. Collected spectra were processed with a peptide fingerprint algorithm, and searching parameters were optimized for the detection of SARS-CoV-2. These peptides provide high sequence coverage for nucleocapsid (N protein) and allow confident identification of SARS-CoV-2. Peptide markers contributing to the detection were rigorously identified using bottom-up proteomics. The approach demonstrated in this study holds the potential for developing a rapid assay for COVID-19 diagnosis and detecting virus variants from a variety of sources, such as sewage and nasal swabs.

Mask assistance to colorimetric sniffers for detection of Covid-19 disease using exhaled breath metabolites.

According to World Health Organization reports, large numbers of people around the globe have been infected or died for Covid-19 due to the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Researchers are still trying to find a rapid and accurate diagnostic method for revealing infected people by low viral load with the overriding goal of effective diagnostic management. Monitoring the body metabolic changes is known as an effective and inexpensive approach for the evaluation of the infected people. Here, an optical sniffer is introduced to detect exhaled breath metabolites of patients with Covid-19 (60 samples), healthy humans (55 samples), and cured people (15 samples), providing a unique color pattern for differentiation between the studied samples. The sniffer device is installed on a thin face mask, and directly exposed to the exhaled breath stream. The interactions occurring between the volatile compounds and sensing components such as porphyrazines, modified organic dyes, porphyrins, inorganic complexes, and gold nanoparticles allowing for the change of the color, thus being tracked as the sensor responses. The assay accuracy for the differentiation between patient, healthy and cured samples is calculated to be in the range of 80%-84%. The changes in the color of the sensor have a linear correlation with the disease severity and viral load evaluated by rRT-PCR method. Interestingly, comorbidities such as kidney, lung, and diabetes diseases as well as being a smoker may be diagnosed by the proposed method. As a powerful detection device, the breath sniffer can replace the conventional rapid test kits for medical applications.

SARS-Net: COVID-19 detection from chest x-rays by combining graph convolutional network and convolutional neural network.

COVID-19 has emerged as one of the deadliest pandemics that has ever crept on humanity. Screening tests are currently the most reliable and accurate steps in detecting severe acute respiratory syndrome coronavirus in a patient, and the most used is RT-PCR testing. Various researchers and early studies implied that visual indicators (abnormalities) in a patient's Chest X-Ray (CXR) or computed tomography (CT) imaging were a valuable characteristic of a COVID-19 patient that can be leveraged to find out virus in a vast population. Motivated by various contributions to open-source community to tackle COVID-19 pandemic, we introduce SARS-Net, a CADx system combining Graph Convolutional Networks and Convolutional Neural Networks for detecting abnormalities in a patient's CXR images for presence of COVID-19 infection in a patient. In this paper, we introduce and evaluate the performance of a custom-made deep learning architecture SARS-Net, to classify and detect the Chest X-ray images for COVID-19 diagnosis. Quantitative analysis shows that the proposed model achieves more accuracy than previously mentioned state-of-the-art methods. It was found that our proposed model achieved an accuracy of 97.60% and a sensitivity of 92.90% on the validation set.

SARS-CoV-2 Detection Using Optical Fiber Based Sensor Method.

The SARS-CoV-2 Coronavirus disease, also known as the COVID-19 pandemic, has engendered the biggest challenge to human life for the last two years. With a rapid increase in the spread of the Omicron variant across the world, and to contain the spread of COVID-19 in general, it is crucial to rapidly identify this viral infection with minimal logistics. To achieve this, a novel plastic optical fiber (POF) U-shaped probe sensing method is presented for accurate detection of SARS-CoV-2, commonly known as the COVID-19 virus, which has the capability to detect new variants such as Omicron. The sample under test can be taken from oropharyngeal or nasopharyngeal via specific POF U-shaped probe with one end that is fed with a laser source while the other end is connected to a photodetector to receive the response and postprocess for decision-making. The study includes detection comparison with two types of POF with diameters of 200 and 500 µm. Results show that detection is better when a smaller-diameter POF is used. It is also seen that the proposed test bed and its envisaged prototype can detect the COVID-19 variants within 15 min of the test. The proposed approach will make the clinical diagnosis faster, cheaper and applicable to patients in remote areas where there are no hospitals or clinical laboratories due to poverty, geographic obstacles, or other factors.

FedSGDCOVID: Federated SGD COVID-19 Detection under Local Differential Privacy Using Chest X-ray Images and Symptom Information.

Coronavirus (COVID-19) has created an unprecedented global crisis because of its detrimental effect on the global economy and health. COVID-19 cases have been rapidly increasing, with no sign of stopping. As a result, test kits and accurate detection models are in short supply. Early identification of COVID-19 patients will help decrease the infection rate. Thus, developing an automatic algorithm that enables the early detection of COVID-19 is essential. Moreover, patient data are sensitive, and they must be protected to prevent malicious attackers from revealing information through model updates and reconstruction. In this study, we presented a higher privacy-preserving federated learning system for COVID-19 detection without sharing data among data owners. First, we constructed a federated learning system using chest X-ray images and symptom information. The purpose is to develop a decentralized model across multiple hospitals without sharing data. We found that adding the spatial pyramid pooling to a 2D convolutional neural network improves the accuracy of chest X-ray images. Second, we explored that the accuracy of federated learning for COVID-19 identification reduces significantly for non-independent and identically distributed (Non-IID) data. We then proposed a strategy to improve the model's accuracy on Non-IID data by increasing the total number of clients, parallelism (client-fraction), and computation per client. Finally, for our federated learning model, we applied a differential privacy stochastic gradient descent (DP-SGD) to improve the privacy of patient data. We also proposed a strategy to maintain the robustness of federated learning to ensure the security and accuracy of the model.

COVID-19 prognosis using limited chest X-ray images.

The COrona VIrus Disease 2019 (COVID-19) pandemic is an ongoing global pandemic that has claimed millions of lives till date. Detecting COVID-19 and isolating affected patients at an early stage is crucial to contain its rapid spread. Although accurate, the primary viral test 'Reverse Transcription Polymerase Chain Reaction' (RT-PCR) for COVID-19 diagnosis has an elaborate test kit, and the turnaround time is high. This has motivated the research community to develop CXR based automated COVID-19 diagnostic methodologies. However, COVID-19 being a novel disease, there is no annotated large-scale CXR dataset for this particular disease. To address the issue of limited data, we propose to exploit a large-scale CXR dataset collected in the pre-COVID era and train a deep neural network in a self-supervised fashion to extract CXR specific features. Further, we compute attention maps between the global and the local features of the backbone convolutional network while finetuning using a limited COVID-19 CXR dataset. We empirically demonstrate the effectiveness of the proposed method. We provide a thorough ablation study to understand the effect of each proposed component. Finally, we provide visualizations highlighting the critical patches instrumental to the predictive decision made by our model. These saliency maps are not only a stepping stone towards explainable AI but also aids radiologists in localizing the infected area.

BLCov: A novel collaborative-competitive broad learning system for COVID-19 detection from radiology images.

With the global outbreak of COVID-19, there is an urgent need to develop an effective and automated detection approach as a faster diagnostic alternative to avoid the spread of COVID-19. Recently, broad learning system (BLS) has been viewed as an alternative method of deep learning which has been applied to many areas. Nevertheless, the sparse autoencoder in classical BLS just considers the representations to reconstruct the input data but ignores the relationship among the extracted features. In this paper, inspired by the effectiveness of the collaborative-competitive representation (CCR) mechanism, a novel collaborative-competitive representation-based autoencoder (CCRAE) is first proposed, and then collaborative-competitive broad learning system (CCBLS) is proposed based on CCRAE to effectively address the issues mentioned above. Moreover, an automated CCBLS-based approach is proposed for COVID-19 detection from radiology images such as CT scans and chest X-ray images. In the proposed approach, a feature extraction module is utilized to extract features from CT scans or chest X-ray images, then we use these features for COVID-19 detection with CCBLS. The experimental results demonstrated that our proposed approach can achieve superior or comparable performance in comparison with ten other state-of-the-art methods.

COVID-19 detection from CT scans using a two-stage framework.

Coronavirus disease 2019 (COVID-19) is a contagious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). It may cause serious ailments in infected individuals and complications may lead to death. X-rays and Computed Tomography (CT) scans can be used for the diagnosis of the disease. In this context, various methods have been proposed for the detection of COVID-19 from radiological images. In this work, we propose an end-to-end framework consisting of deep feature extraction followed by feature selection (FS) for the detection of COVID-19 from CT scan images. For feature extraction, we utilize three deep learning based Convolutional Neural Networks (CNNs). For FS, we use a meta-heuristic optimization algorithm, Harmony Search (HS), combined with a local search method, Adaptive ß -Hill Climbing (A ß HC) for better performance. We evaluate the proposed approach on the SARS-COV-2 CT-Scan Dataset consisting of 2482 CT scan images and an updated version of the previous dataset containing 2926 CT scan images. For comparison, we use a few state-of-the-art optimization algorithms. The best accuracy scores obtained by the present approach are 97.30% and 98.87% respectively on the said datasets, which are better than many of the algorithms used for comparison. The performances are also at par with some recent works which use the same datasets. The codes for the FS algorithms are available at: https://github.com/khalid0007/Metaheuristic-Algorithms.

A deep learning-based framework for detecting COVID-19 patients using chest X-rays.

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has caused outbreaks of new coronavirus disease (COVID-19) around the world. Rapid and accurate detection of COVID-19 coronavirus is an important step in limiting the spread of the COVID-19 epidemic. To solve this problem, radiography techniques (such as chest X-rays and computed tomography (CT)) can play an important role in the early prediction of COVID-19 patients, which will help to treat patients in a timely manner. We aimed to quickly develop a highly efficient lightweight CNN architecture for detecting COVID-19-infected patients. The purpose of this paper is to propose a robust deep learning-based system for reliably detecting COVID-19 from chest X-ray images. First, we evaluate the performance of various pre-trained deep learning models (InceptionV3, Xception, MobileNetV2, NasNet and DenseNet201) recently proposed for medical image classification. Second, a lightweight shallow convolutional neural network (CNN) architecture is proposed for classifying X-ray images of a patient with a low false-negative rate. The data set used in this work contains 2,541 chest X-rays from two different public databases, which have confirmed COVID-19 positive and healthy cases. The performance of the proposed model is compared with the performance of pre-trained deep learning models. The results show that the proposed shallow CNN provides a maximum accuracy of 99.68% and more importantly sensitivity, specificity and AUC of 99.66%, 99.70% and 99.98%. The proposed model has fewer parameters and low complexity compared to other deep learning models. The experimental results of our proposed method show that it is superior to the existing state-of-the-art methods. We believe that this model can help healthcare professionals to treat COVID-19 patients through improved and faster patient screening.

COFE-Net: An ensemble strategy for Computer-Aided Detection for COVID-19.

Biomedical images contain a large volume of sensor measurements, which can reveal the descriptors of the disease under investigation. Computer-based analysis of such measurements helps detect the disease, and thereby swiftly aid medical professionals to choose adequate therapy. In this paper, we propose a robust deep learning ensemble framework known as COVID Fuzzy Ensemble Network, or COFE-Net. This strategy is proposed for the task of COVID-19 screening from chest X-rays (CXR) and CT Scans, as a part of Computer-Aided Detection (CADe) for medical practitioners. We leverage the strategy of Transfer Learning for Convolutional Neural Networks (CNNs) widely adopted in recent literature, and further propose an efficient ensemble network for their combination. The principles of fuzzy logic have been leveraged to combine the measured decision scores generated by three state-of-the-art CNNs - Inception V3, Inception ResNet V2 and DenseNet 201 - through the Choquet fuzzy integral. Experimental results support the efficacy of our approach over empirical ensembling, as the fuzzy ensembling strategy for biomedical measurement consists of dynamic refactoring of the classifier ensemble weights on the fly, based upon the confidence scores for coalitions of inputs. This is the chief advantage of our biomedical measurement strategy over others as other methods do not adjust to the multiple generated measurements dynamically unlike ours.Impressive results on multiple datasets demonstrate the effectiveness of the proposed method. The source code of our proposed method is made available at: https://github.com/theavicaster/covid-cade-ensemble.

Detection of COVID-19 from chest X-ray images: Boosting the performance with convolutional neural network and transfer learning.

Coronavirus disease (COVID-19) is a pandemic that has caused thousands of casualties and impacts all over the world. Most countries are facing a shortage of COVID-19 test kits in hospitals due to the daily increase in the number of cases. Early detection of COVID-19 can protect people from severe infection. Unfortunately, COVID-19 can be misdiagnosed as pneumonia or other illness and can lead to patient death. Therefore, in order to avoid the spread of COVID-19 among the population, it is necessary to implement an automated early diagnostic system as a rapid alternative diagnostic system. Several researchers have done very well in detecting COVID-19; however, most of them have lower accuracy and overfitting issues that make early screening of COVID-19 difficult. Transfer learning is the most successful technique to solve this problem with higher accuracy. In this paper, we studied the feasibility of applying transfer learning and added our own classifier to automatically classify COVID-19 because transfer learning is very suitable for medical imaging due to the limited availability of data. In this work, we proposed a CNN model based on deep transfer learning technique using six different pre-trained architectures, including VGG16, DenseNet201, MobileNetV2, ResNet50, Xception, and EfficientNetB0. A total of 3886 chest X-rays (1200 cases of COVID-19, 1341 healthy and 1345 cases of viral pneumonia) were used to study the effectiveness of the proposed CNN model. A comparative analysis of the proposed CNN models using three classes of chest X-ray datasets was carried out in order to find the most suitable model. Experimental results show that the proposed CNN model based on VGG16 was able to accurately diagnose COVID-19 patients with 97.84% accuracy, 97.90% precision, 97.89% sensitivity, and 97.89% of F1-score. Evaluation of the test data shows that the proposed model produces the highest accuracy among CNNs and seems to be the most suitable choice for COVID-19 classification. We believe that in this pandemic situation, this model will support healthcare professionals in improving patient screening.

A comprehensive review of federated learning for COVID-19 detection.

The coronavirus of 2019 (COVID-19) was declared a global pandemic by World Health Organization in March 2020. Effective testing is crucial to slow the spread of the pandemic. Artificial intelligence and machine learning techniques can help COVID-19 detection using various clinical symptom data. While deep learning (DL) approach requiring centralized data is susceptible to a high risk of data privacy breaches, federated learning (FL) approach resting on decentralized data can preserve data privacy, a critical factor in the health domain. This paper reviews recent advances in applying DL and FL techniques for COVID-19 detection with a focus on the latter. A model FL implementation use case in health systems with a COVID-19 detection using chest X-ray image data sets is studied. We have also reviewed applications of previously published FL experiments for COVID-19 research to demonstrate the applicability of FL in tackling health research issues. Last, several challenges in FL implementation in the healthcare domain are discussed in terms of potential future work.

SAM: Self-augmentation mechanism for COVID-19 detection using chest X-ray images.

COVID-19 is a rapidly spreading viral disease and has affected over 100 countries worldwide. The numbers of casualties and cases of infection have escalated particularly in countries with weakened healthcare systems. Recently, reverse transcription-polymerase chain reaction (RT-PCR) is the test of choice for diagnosing COVID-19. However, current evidence suggests that COVID-19 infected patients are mostly stimulated from a lung infection after coming in contact with this virus. Therefore, chest X-ray (i.e., radiography) and chest CT can be a surrogate in some countries where PCR is not readily available. This has forced the scientific community to detect COVID-19 infection from X-ray images and recently proposed machine learning methods offer great promise for fast and accurate detection. Deep learning with convolutional neural networks (CNNs) has been successfully applied to radiological imaging for improving the accuracy of diagnosis. However, the performance remains limited due to the lack of representative X-ray images available in public benchmark datasets. To alleviate this issue, we propose a self-augmentation mechanism for data augmentation in the feature space rather than in the data space using reconstruction independent component analysis (RICA). Specifically, a unified architecture is proposed which contains a deep convolutional neural network (CNN), a feature augmentation mechanism, and a bidirectional LSTM (BiLSTM). The CNN provides the high-level features extracted at the pooling layer where the augmentation mechanism chooses the most relevant features and generates low-dimensional augmented features. Finally, BiLSTM is used to classify the processed sequential information. We conducted experiments on three publicly available databases to show that the proposed approach achieves the state-of-the-art results with accuracy of 97%, 84% and 98%. Explainability analysis has been carried out using feature visualization through PCA projection and t-SNE plots.

Biosensors as Nano-Analytical Tools for COVID-19 Detection.

Selective, sensitive and affordable techniques to detect disease and underlying health issues have been developed recently. Biosensors as nanoanalytical tools have taken a front seat in this context. Nanotechnology-enabled progress in the health sector has aided in disease and pandemic management at a very early stage efficiently. This report reflects the state-of-the-art of nanobiosensor-based virus detection technology in terms of their detection methods, targets, limits of detection, range, sensitivity, assay time, etc. The article effectively summarizes the challenges with traditional technologies and newly emerging biosensors, including the nanotechnology-based detection kit for COVID-19; optically enhanced technology; and electrochemical, smart and wearable enabled nanobiosensors. The less explored but crucial piezoelectric nanobiosensor and the reverse transcription-loop mediated isothermal amplification (RT-LAMP)-based biosensor are also discussed here. The article could be of significance to researchers and doctors dedicated to developing potent, versatile biosensors for the rapid identification of COVID-19. This kind of report is needed for selecting suitable treatments and to avert epidemics.