Detection of COVID-19 and Pneumonia Using Deep Convolutional Neural Network

COVID-19 has created a panic all around the globe.It is a contagious dis-ease caused by Severe Acute Respiratory Syndrome Coronavirus 2(SARS-CoV-2),originated from Wuhan in December 2019 and spread quickly all over the world.The healthcare sector of the world is facing great challenges tackling COVID cases.One of the problems many have witnessed is the misdiagnosis of COVID-19 cases with that of healthy and pneumonia cases.In this article,we propose a deep Convo-lutional Neural Network(CNN)based approach to detect COVID+(i.e.,patients with COVID-19),pneumonia and normal cases,from the chest X-ray images.COVID-19 detection from chest X-ray is suitable considering all aspects in compar-ison to Reverse Transcription Polymerase Chain Reaction(RT-PCR)and Computed Tomography(CT)scan.Several deep CNN models including VGG16,InceptionV3,DenseNet121,DenseNet201 and InceptionResNetV2 have been adopted in this pro-posed work.They have been trained individually to make particular predictions.Empirical results demonstrate that DenseNet201 provides overall better performance with accuracy,recall,F1-score and precision of 94.75%,96%,95%and 95%respec-tively.After careful comparison with results available in the literature,we have found to develop models with a higher reliability.All the studies were carried out using a publicly available chest X-ray(CXR)image data-set.

Human and ecological risks of metals in soils under different land-use types in an urban environment of Bangladesh

Trace metal contamination in soil is of great concern owing to its long persistence in the environment and toxicity to humans and other organisms.Concentrations of six potentially toxic trace metals,Cr,Ni,Cu,As,Cd,and Pb,in urban soils were measured in Dhaka City,Bangladesh.Soils from different land-use types,namely,agricultural field,park,playground,petrol station,metal workshop,brick field,burning sites,disposal sites of household waste,garment waste,electronic waste,and tannery wast,and construction waste demolishing sites,were investigated.The concentration ranges of Cr,Ni,Cu,As,Pb,and Cd in soils were 2.4–1258,8.3–1044,9.7–823,8.7–277,1.8–80,and 13–842 mg kg^-1,respectively.The concentrations of metals were subsequently used to establish hazard quotients(HQs)for the adult population.The metal HQs decreased in the order of As>Cr>Pb>Cd>Ni>Cu.Ingestion was the most vital exposure pathway of studied metals from soils followed by dermal contact and inhalation.The range of pollution load index(PLI)was 0.96–17,indicating severe contamination of soil by trace metals.Considering the comprehensive potential ecological risk(PER),soils from all land-use types showed considerable to very high ecological risks.The findings of this study revealed that in the urban area studied,soils of some land-use types were severely contaminated with trace metals.Thus,it is suggested that more attention should be paid to the potential health risks to the local inhabitants and ecological risk to the surrounding ecosystems.

Is the Trade-led Growth Hypothesis Valid for the Kingdom of Saudi Arabia?Evidence from an ARDL Approach

This study examines the trade-led growth(TLG)hypothesis for the Kingdom of Saudi Arabia.Using time-series annual data for the period 1985-2019,the ARDL approach and Toda-Yamamoto Granger causality test are applied to accomplish the study.The ARDL estimation reveals that trade openness positively causes economic growth in both the long and short run,and the TLG hypothesis is found valid for the Kingdom.The Toda-Yamamoto Granger causality test results have evidenced several unidirectional causalities.Of them,trade openness causes economic growth and supports the ARDL finding and hence the TLG hypothesis for the Kingdom.Moreover,trade openness causes gross fixed capital formation,and the labor force stimulates both economic growth and trade volume.The findings recommend that the Kingdom may increase its trade to reap further benefits and enhance its income growth.