scholarly journals Deep Learning Based Early Detection Framework for Preliminary Diagnosis of COVID-19 via Onboard Smartphone Sensors

Sensors ◽  
2021 ◽  
Vol 21 (20) ◽  
pp. 6853
Author(s):  
Hayat Khaloufi ◽  
Karim Abouelmehdi ◽  
Abderrahim Beni-Hssane ◽  
Furqan Rustam ◽  
Anca Delia Jurcut ◽  
...  
Keyword(s):  
Deep Learning ◽  
Early Detection ◽  
Cost Effective ◽  
Clinical Analysis ◽  
Test Methods ◽  
Nucleic Acid Amplification ◽  
Micro Electro Mechanical Systems ◽  
Preliminary Diagnosis ◽  
Computer Tomography Scan ◽  
Smartphone Sensors

The COVID-19 pandemic has affected almost every country causing devastating economic and social disruption and stretching healthcare systems to the limit. Furthermore, while being the current gold standard, existing test methods including NAAT (Nucleic Acid Amplification Tests), clinical analysis of chest CT (Computer Tomography) scan images, and blood test results, require in-person visits to a hospital which is not an adequate way to control such a highly contagious pandemic. Therefore, top priority must be given, among other things, to enlisting recent and adequate technologies to reduce the adverse impact of this pandemic. Modern smartphones possess a rich variety of embedded MEMS (Micro-Electro-Mechanical-Systems) sensors capable of recording movements, temperature, audio, and video of their carriers. This study leverages the smartphone sensors for the preliminary diagnosis of COVID-19. Deep learning, an important breakthrough in the domain of artificial intelligence in the past decade, has huge potential for extracting apt and appropriate features in healthcare. Motivated from these facts, this paper presents a new framework that leverages advanced machine learning and data analytics techniques for the early detection of coronavirus disease using smartphone embedded sensors. The proposal provides a simple to use and quickly deployable screening tool that can be easily configured with a smartphone. Experimental results indicate that the model can detect positive cases with an overall accuracy of 79% using only the data from the smartphone sensors. This means that the patient can either be isolated or treated immediately to prevent further spread, thereby saving more lives. The proposed approach does not involve any medical tests and is a cost-effective solution that provides robust results.

scholarly journals Role of Deep Learning in Early Detection of COVID-19: Scoping Review

2021 ◽  
pp. 100025
Author(s):  
Mahmood Alzubaidi ◽  
Haider Dhia Zubaydi ◽  
Ali Bin-Salem ◽  
Alaa A Abd-Alrazaq ◽  
Arfan Ahmed ◽  
...  
Keyword(s):  
Deep Learning ◽  
Early Detection ◽  
Scoping Review

scholarly journals Food Allergen Detection Methods: A Coordinated Approach

2004 ◽  
Vol 87 (6) ◽  
pp. 1383-1390 ◽  
Author(s):  
Philip R Goodwin
Keyword(s):  
Hazard Analysis ◽  
Adult Population ◽  
Control Point ◽  
Cost Effective ◽  
Test Methods ◽  
Detection Methods ◽  
Critical Control Point ◽  
Life Threatening ◽  
Monitoring And Surveillance ◽  
Analytical Approaches

Abstract The levels (1–2%) and increasing severity of allergic responses to food in the adult population are well documented, as is the phenomenon of even higher (3–8%) and apparently increasing incidence in children, albeit that susceptibility decreases with age. Problematic foods include peanut, milk, eggs, tree nuts, and sesame, but the list is growing as awareness continues to rise. The amounts of such foods that can cause allergic reactions is difficult to gauge; however, the general consensus is that ingestion of low parts per million is sufficient to cause severe reactions in badly affected individuals. Symptoms can rapidly—within minutes—progress from minor discomfort to severe, even life-threatening anaphylactic shock in those worst affected. Given the combination of high incidence of atopy, potential severity of response, and apparently widespread instances of “hidden” allergens in the food supply, it is not surprising that this issue is increasingly subject to legislative and regulatory scrutiny. In order to assist in the control of allergen levels in foods to acceptable levels, analysts require a combination of test methods, each designed to produce accurate, timely, and cost-effective analytical information. Such information contributes significantly to Hazard Analysis Critical Control Point programs to determine food manufacturers’ risk and improves the accuracy of monitoring and surveillance by food industry, commercial, and enforcement laboratories. Analysis thereby facilitates improvements in compliance with labeling laws with concomitant reductions in risks to atopic consumers. This article describes a combination of analytical approaches to fulfill the various needs of these 3 analytical communities.

scholarly journals Early Detection of Parkinson’s Disease Using Deep Learning and Machine Learning

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 147635-147646 ◽  
Author(s):  
Wu Wang ◽  
Junho Lee ◽  
Fouzi Harrou ◽  
Ying Sun
Keyword(s):  
Machine Learning ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Deep Learning ◽  
Early Detection

scholarly journals Early Detection of Plant Viral Disease Using Hyperspectral Imaging and Deep Learning

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 742
Author(s):  
Canh Nguyen ◽  
Vasit Sagan ◽  
Matthew Maimaitiyiming ◽  
Maitiniyazi Maimaitijiang ◽  
Sourav Bhadra ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Early Detection ◽  
Convolutional Neural Network ◽  
Near Infrared ◽  
Hyperspectral Data ◽  
Viral Diseases ◽  
Support Vector ◽  
Spectral Features ◽  
Feature Spaces

Early detection of grapevine viral diseases is critical for early interventions in order to prevent the disease from spreading to the entire vineyard. Hyperspectral remote sensing can potentially detect and quantify viral diseases in a nondestructive manner. This study utilized hyperspectral imagery at the plant level to identify and classify grapevines inoculated with the newly discovered DNA virus grapevine vein-clearing virus (GVCV) at the early asymptomatic stages. An experiment was set up at a test site at South Farm Research Center, Columbia, MO, USA (38.92 N, −92.28 W), with two grapevine groups, namely healthy and GVCV-infected, while other conditions were controlled. Images of each vine were captured by a SPECIM IQ 400–1000 nm hyperspectral sensor (Oulu, Finland). Hyperspectral images were calibrated and preprocessed to retain only grapevine pixels. A statistical approach was employed to discriminate two reflectance spectra patterns between healthy and GVCV vines. Disease-centric vegetation indices (VIs) were established and explored in terms of their importance to the classification power. Pixel-wise (spectral features) classification was performed in parallel with image-wise (joint spatial–spectral features) classification within a framework involving deep learning architectures and traditional machine learning. The results showed that: (1) the discriminative wavelength regions included the 900–940 nm range in the near-infrared (NIR) region in vines 30 days after sowing (DAS) and the entire visual (VIS) region of 400–700 nm in vines 90 DAS; (2) the normalized pheophytization index (NPQI), fluorescence ratio index 1 (FRI1), plant senescence reflectance index (PSRI), anthocyanin index (AntGitelson), and water stress and canopy temperature (WSCT) measures were the most discriminative indices; (3) the support vector machine (SVM) was effective in VI-wise classification with smaller feature spaces, while the RF classifier performed better in pixel-wise and image-wise classification with larger feature spaces; and (4) the automated 3D convolutional neural network (3D-CNN) feature extractor provided promising results over the 2D convolutional neural network (2D-CNN) in learning features from hyperspectral data cubes with a limited number of samples.

scholarly journals Spectroscopic detection of forest diseases: a review (1970–2020)

2021 ◽  
Author(s):  
Lorenzo Cotrozzi
Keyword(s):  
Early Detection ◽  
Optical Sensors ◽  
Near Infrared ◽  
Sustainable Forest Management ◽  
Visible Spectrum ◽  
Cost Effective ◽  
Infrared Region ◽  
Forest Disturbances ◽  
Effective Manner ◽  
Forest Diseases

AbstractSustainable forest management is essential to confront the detrimental impacts of diseases on forest ecosystems. This review highlights the potential of vegetation spectroscopy in improving the feasibility of assessing forest disturbances induced by diseases in a timely and cost-effective manner. The basic concepts of vegetation spectroscopy and its application in phytopathology are first outlined then the literature on the topic is discussed. Using several optical sensors from leaf to landscape-level, a number of forest diseases characterized by variable pathogenic processes have been detected, identified and quantified in many country sites worldwide. Overall, these reviewed studies have pointed out the green and red regions of the visible spectrum, the red-edge and the early near-infrared as the spectral regions most sensitive to the disease development as they are mostly related to chlorophyll changes and symptom development. Late disease conditions particularly affect the shortwave-infrared region, mostly related to water content. This review also highlights some major issues to be addressed such as the need to explore other major forest diseases and geographic areas, to further develop hyperspectral sensors for early detection and discrimination of forest disturbances, to improve devices for remote sensing, to implement long-term monitoring, and to advance algorithms for exploitation of spectral data. Achieving of these goals will enhance the capability of vegetation spectroscopy in early detection of forest stress and in managing forest diseases.

Deep-n-Cheap: An Automated Efficient and Extensible Search Framework for Cost-Effective Deep Learning

2021 ◽  
Vol 2 (4) ◽  
Author(s):  
Sourya Dey ◽  
Sara Babakniya ◽  
Saikrishna C. Kanala ◽  
Marco Paolieri ◽  
Leana Golubchik ◽  
...  
Keyword(s):  
Deep Learning ◽  
Cost Effective

scholarly journals Evolving Deep Learning Convolutional Neural Networks for Early COVID-19 Detection in Chest X-ray Images

Mathematics ◽  
2021 ◽  
Vol 9 (9) ◽  
pp. 1002
Author(s):  
Mohammad Khishe ◽  
Fabio Caraffini ◽  
Stefan Kuhn
Keyword(s):  
Deep Learning ◽  
Early Detection ◽  
Iterative Process ◽  
High Accuracy ◽  
X Ray ◽  
Starting Point ◽  
Reliable Model ◽  
Minimum Number ◽  
Chest X Ray ◽  
Deep Learning Model

This article proposes a framework that automatically designs classifiers for the early detection of COVID-19 from chest X-ray images. To do this, our approach repeatedly makes use of a heuristic for optimisation to efficiently find the best combination of the hyperparameters of a convolutional deep learning model. The framework starts with optimising a basic convolutional neural network which represents the starting point for the evolution process. Subsequently, at most two additional convolutional layers are added, at a time, to the previous convolutional structure as a result of a further optimisation phase. Each performed phase maximises the the accuracy of the system, thus requiring training and assessment of the new model, which gets gradually deeper, with relevant COVID-19 chest X-ray images. This iterative process ends when no improvement, in terms of accuracy, is recorded. Hence, the proposed method evolves the most performing network with the minimum number of convolutional layers. In this light, we simultaneously achieve high accuracy while minimising the presence of redundant layers to guarantee a fast but reliable model. Our results show that the proposed implementation of such a framework achieves accuracy up to 99.11%, thus being particularly suitable for the early detection of COVID-19.

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