RFID Adaption in Healthcare Organizations:An Integrative Framework

Radio frequency identification(RFID),also known as electronic label technology,is a non-contact automated identification technology that recognizes the target object and extracts relevant data and critical characteristics using radio frequency signals.Medical equipment information management is an important part of the construction of a modern hospital,as it is linked to the degree of diagnosis and care,as well as the hospital’s benefits and growth.The aim of this study is to create an integrated view of a theoretical framework to identify factors that influence RFID adoption in healthcare,as well as to conduct an empirical review of the impact of organizational,environmental,and individual factors on RFID adoption in the healthcare industry.In contrast to previous research,the current study focuses on individual factors as well as organizational and technological factors in order to better understand the phenomenon of RFID adoption in healthcare,which is characterized as a dynamic and challenging work environment.This research fills a gap in the current literature by describing how user factors can influence RFID adoption in healthcare and how such factors can lead to a deeper understanding of the advantages,uses,and impacts of RFID in healthcare.The proposed study has superior performance and effective results.

Early Diagnosis of Alzheimer’s Disease Based on Convolutional Neural Networks

Alzheimer’s disease(AD)is a neurodegenerative disorder,causing the most common dementia in the elderly peoples.The AD patients are rapidly increasing in each year and AD is sixth leading cause of death in USA.Magnetic resonance imaging(MRI)is the leading modality used for the diagnosis of AD.Deep learning based approaches have produced impressive results in this domain.The early diagnosis of AD depends on the efficient use of classification approach.To address this issue,this study proposes a system using two convolutional neural networks(CNN)based approaches for an early diagnosis of AD automatically.In the proposed system,we use segmented MRI scans.Input data samples of three classes include 110 normal control(NC),110 mild cognitive impairment(MCI)and 105 AD subjects are used in this paper.The data is acquired from the ADNI database and gray matter(GM)images are obtained after the segmentation of MRI subjects which are used for the classification in the proposed models.The proposed approaches segregate among NC,MCI,and AD.While testing both methods applied on the segmented data samples,the highest performance results of the classification in terms of accuracy on NC vs.AD are 95.33%and 89.87%,respectively.The proposed methods distinguish between NC vs.MCI and MCI vs.AD patients with a classification accuracy of 90.74%and 86.69%.The experimental outcomes prove that both CNN-based frameworks produced state-of-the-art accurate results for testing.

Smart COVID-3D-SCNN: A Novel Method to Classify X-ray Images of COVID-19

The outbreak of the novel coronavirus has spread worldwide,and millions of people are being infected.Image or detection classification is one of the first application areas of deep learning,which has a significant contribution to medical image analysis.In classification detection,one or more images(detection)are usually used as input,and diagnostic variables(such as whether there is a disease)are used as output.The novel coronavirus has spread across the world,infecting millions of people.Early-stage detection of critical cases of COVID-19 is essential.X-ray scans are used in clinical studies to diagnose COVID-19 and Pneumonia early.For extracting the discriminative features through these modalities,deep convolutional neural networks(CNNs)are used.A siamese convolutional neural network model(COVID-3D-SCNN)is proposed in this study for the automated detection of COVID-19 by utilizing X-ray scans.To extract the useful features,we used three consecutive models working in parallel in the proposed approach.We acquired 575 COVID-19,1200 non-COVID,and 1400 pneumonia images,which are publicly available.In our framework,augmentation is used to enlarge the dataset.The findings suggest that the proposed method outperforms the results of comparative studies in terms of accuracy 96.70%,specificity 95.55%,and sensitivity 96.62%over(COVID-19 vs.non-COVID19 vs.Pneumonia).