Automatic brain tumor segmentation from magnetic resonance images using superpixel-based approach

Multimedia Tools and Applications - Cancer is the second leading cause of deaths worldwide, reported by World Health Organization (WHO). The abnormal growth of cells, which should die at the time...     

Segmentation and quantification of COVID-19 infections in CT using pulmonary vessels extraction and deep learning

Multimedia Tools and Applications - At the end of 2019, the World Health Organization (WHO) reported pneumonia that started in Wuhan, China, as a global emergency problem. Researchers quickly...     

Description of philophonetics counselling as expressive therapeutic modality for treating depression

AI & SOCIETY - Depression is ranked as most common type of mental illness by the World Health Organization (WHO in Depression and other common mental disorders: Global health estimates. World...     

Covid-19’s Pandemic Relationship to Saudi Arabia’s Weather Using Statistical Analysis and GIS

The eruption of the novel Covid-19 has changed the socio-economic conditions of the world. The escalating number of infections and deaths seriously threatened human health when it became a pandemic from an epidemic. It developed into an alarming situation when the World Health Organization (WHO) declared a health emergency in MARCH 2020. The geographic settings and weather conditions are systematically linked to the spread of the epidemic. The concentration of population and weather attributes remains vital to study a pandemic such as Covid-19. The current work aims to explore the relationship of the population, weather conditions (humidity and temperature) with the reported novel Covid-19 cases in the Kingdom of Saudi Arabia (KSA). For the study, the data for the reported Covid-19 cases was secured from 11 March 2020, to 21 July 2020 (132 days) from the 13 provinces of KSA. The Governorate level data was used to estimate the population data. A Geographic information system (GIS) analysis was utilised to visualise the relationship. The results suggested that a significant correlation existed between the population and Covid-19 cases. For the weather conditions, the data for the 13 provinces of KSA for the same period was utilised to estimate the relationship between the weather conditions and Covid-19 cases. Spearman’s rank correlation results confirmed that the humidity was significantly linked with the reported cases of Covid-19 in Makkah, Aseer, Najran, and Al Baha provinces. The temperature had a significant relation with the reported Covid-19 cases in Al-Riyad, Makkah, Al-Madinah, Aseer, Najran, and Al-Baha. The inconsistency of the results highlighted the variant behavior of Covid-19 in different regions of the KSA. More exploration is required beyond the weather-related variables. Suggestions for future research and policy direction are offered at the end of the study.     

Learning Patterns from COVID-19 Instances

Coronavirus disease, which resulted from the SARS-CoV-2 virus, has spread worldwide since early 2020 and has been declared a pandemic by the World Health Organization (WHO). Coronavirus disease is also termed COVID-19. It affects the human respiratory system and thus can be traced and tracked from the Chest X-Ray images. Therefore, Chest X-Ray alone may play a vital role in identifying COVID-19 cases. In this paper, we propose a Machine Learning (ML) approach that utilizes the X-Ray images to classify the healthy and affected patients based on the patterns found in these images. The article also explores traditional, and Deep Learning (DL) approaches for COVID-19 patterns from Chest X-Ray images to predict, analyze, and further understand this virus. The experimental evaluation of the proposed approach achieves 97.5% detection performance using the DL model for COVID-19 versus normal cases. In contrast, for COVID-19 versus Pneumonia Virus scenario, we achieve 94.5% accurate detections. Our extensive evaluation in the experimental section guides and helps in the selection of an appropriate model for similar tasks. Thus, the approach can be used for medical usages and is particularly pertinent in detecting COVID-19 positive patients using X-Ray images alone.     

Analyzing the Implications of COVID-19 Pandemic through an Intelligent-Computing Technique

The outbreak of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2 virus or COVID-19) disease was declared pandemic by the World Health Organization (WHO) on March 11, 2020. COVID-19 has already affected more than 211 nations. In such a bleak scenario, it becomes imperative to analyze and identify those regions in Saudi Arabia that are at high risk. A preemptive study done in the context of predicting the possible COVID-19 hotspots would facilitate in the implementation of prompt and targeted countermeasures against SARS-CoV-2, thus saving many lives. Working towards this intent, the present study adopts a decision making based methodology of simulation named Analytical Hierarchy Process (AHP), a multi criteria decision making approach, for assessing the risk of COVID-19 in different regions of Saudi Arabia. AHP gives the ability to measure the risks numerically. Moreover, numerical assessments are always effective and easy to understand. Hence, this research endeavour employs Fuzzy based computational method of decision making for its empirical analysis. Findings in the proposed paper suggest that Riyadh and Makkah are the most susceptible regions, implying that if sustained and focused preventive measures are not introduced at the right juncture, the two cities could be the worst afflicted with the infection. The results obtained through Fuzzy based computational method of decision making are highly corroborative and would be very useful for categorizing and assessing the current COVID-19 situation in the Kingdom of Saudi Arabia. More specifically, identifying the cities that are likely to be COVID-19 hotspots would help the country’s health and medical fraternity to reinforce intensive containment strategies to counter the ills of the pandemic in such regions.     

Artificial intelligence (AI) for medical imaging to combat coronavirus disease (COVID-19): a detailed review with direction for future research

Artificial Intelligence Review - Since early 2020, the whole world has been facing the deadly and highly contagious disease named coronavirus disease (COVID-19) and the World Health Organization...     

Selective ensemble based on extreme learning machine and improved discrete artificial fish swarm algorithm for haze forecast

Urban haze pollution is becoming increasingly serious, which is considered very harmful for humans by World Health Organization (WHO). Haze forecasts can be used to protect human health. In this paper, a Selective ENsemble based on an Extreme Learning Machine (ELM) and Improved Discrete Artificial Fish swarm algorithm (IDAFSEN) is proposed, which overcomes the drawback that a single ELM is unstable in terms of its classification. First, the initial pool of base ELMs is generated by using bootstrap sampling, which is then pre-pruned by calculating the pair-wise diversity measure of each base ELM. Second, partial-based ELMs among the initial pool after pre-pruning with higher precision and with greater diversity are selected by using an Improved Discrete Artificial Fish Swarm Algorithm (IDAFSA). Finally, the selected base ELMs are integrated through majority voting. The Experimental results on 16 datasets from the UCI Machine Learning Repository demonstrate that IDAFSEN can achieve better classification accuracy than other previously reported methods. After a performance evaluation of the proposed approach, this paper looks at how this can be used in haze forecasting in China to protect human health.     

Automatic detection of potential mosquito breeding sites from aerial images acquired by unmanned aerial vehicles

The World Health Organization (WHO) has stated that effective vector control measures are critical to achieving and sustaining reduction of vector-borne infectious disease incidence. Unmanned aerial vehicles (UAVs), popularly known as drones, can be an important technological tool for health surveillance teams to locate and eliminate mosquito breeding sites in areas where vector-borne diseases such as dengue, zika, chikungunya or malaria are endemic, since they allow the acquisition of aerial images with high spatial and temporal resolution. Currently, though, such images are often analyzed through manual processes that are excessively time-consuming when implementing vector control interventions. In this work we propose computational approaches for the automatic identification of objects and scenarios suspected of being potential mosquito breeding sites from aerial images acquired by drones. These approaches were developed using convolutional neural networks (CNN) and Bag of Visual Words combined with the Support Vector Machine classifier (BoVW + SVM), and their performances were evaluated in terms of mean Average Precision - mAP-50. In the detection of objects using a CNN YOLOv3 model the rate of 0.9651 was obtained for the mAP-50. In the detection of scenarios, in which the performances of BoVW+SVM and a CNN YOLOv3 were compared, the respective rates of 0.6453 and 0.9028 were obtained. These findings indicate that the proposed CNN-based approaches can be used to identify potential mosquito breeding sites from images acquired by UAVs, providing substantial improvements in vector control programs aiming the reduction of mosquito-breeding sources in the environment.     

Ensemble feature selection and classification methods for machine learning-based coronary artery disease diagnosis

Coronary artery disease (CAD) is a condition in which the heart is not fed sufficiently as a result of the accumulation of fatty matter. As reported by the World Health Organization, around 32% of the total deaths in the world are caused by CAD, and it is estimated that approximately 23.6 million people will die from this disease in 2030. CAD develops over time, and the diagnosis of this disease is difficult until a blockage or a heart attack occurs. In order to bypass the side effects and high costs of the current methods, researchers have proposed to diagnose CADs with computer-aided systems, which analyze some physical and biochemical values at a lower cost. In this study, for the CAD diagnosis, (i) seven different computational feature selection (FS) methods, one domain knowledge-based FS method, and different classification algorithms have been evaluated; (ii) an exhaustive ensemble FS method and a probabilistic ensemble FS method have been proposed. The proposed approach is tested on three publicly available CAD data sets using six different classification algorithms and four different variants of voting algorithms. The performance metrics have been comparatively evaluated with numerous combinations of classifiers and FS methods. The multi-layer perceptron classifier obtained satisfactory results on three data sets. Performance evaluations show that the proposed approach resulted in 91.78%, 85.55%, and 85.47% accuracy for the Z-Alizadeh Sani, Statlog, and Cleveland data sets, respectively.     

Deep Convolutional Neural Network (Falcon) and transfer learning‐based approach to detect malarial parasite

Deep learning models have already benchmarked its demonstration in the applications of Medical Sciences. Present day medical industries suffer due to deadly disease such as malaria etc. As per the report from World Health Organization (WHO), it is noted that the amount of caution and care taken per patient by a human doctor to cure malaria is decreasing. To address this issue, this paper proposes an automated solution for the detection of malaria from the real-time image. The key idea of the proposed solution is to use a Deep Convolutional Neural Network (DCNN) called “Falcon” to detect the parasitic cells from blood smeared slide images of Malaria Screener. Furthermore, the class accuracy of the given dataset samples is maintained in order to model not only the normal case but to accurately predict the presence of malaria as well. Experimental results confirms that the model does not possess overfitting, class imbalance, and provides a reasonable classification report and trustworthy accuracy with 95.2?% when compared to the state-of-the-art Convolutional Neural Network (CNN) models.

     

臭氧处理对石墨烯基NO_2气体传感器气敏性影响

通过臭氧处理制备了一种简单、高效、可重复使用的单层石墨烯基NO2气体传感器,并研究了纯的和经过臭氧处理的NO2气体传感器的响应特性和恢复特性。实验表明:经臭氧处理75s后的石墨烯基气体传感器对10×10-6NO2响应度可达到30%以上,是未经臭氧处理的石墨烯基气体传感器的2.8倍,且其响应的最低体积分数达150×10-9,低于世界卫生组织(WHO)空气质量标准(200×10-9,1h平均值)。     

OptiPres: a distributed mobile agent decision support system for optimal patient drug prescription

Prescribing the right drugs for a patient is a difficult task that takes into consideration several factors. The Institute of Medicine (IOM), U.S.A., has reported based on two major studies (1999–2001 & 2006) that prescribing the wrong medication is a big problem, and the effects can sometimes be fatal. To address this problem, we designed and implemented, a distributed intelligent mobile agent-based system by the name, OptiPres. This system will be used by doctors on their smart phones while prescribing medicines. It will assist them in making more informed decisions by either choosing the optimal solution from processing a repository of past decisions or by presenting a set of possible drugs and using criteria specified by them to identify the optimal drug. The evaluation of OptiPres was done by comparing its recommended outcome of three predefined medical scenarios against the recommendations from a group of doctors and the World Health Organization (WHO) manual entitled:‘Guide to Good Prescribing’. The results indicate that OptiPres is effective in prescribing optimal drugs and in reducing the cognitive burden on doctors, especially in subjective decision making contexts where they have to consider multiple parameters.     

Breast cancer classification using deep belief networks

Over the last decade, the ever increasing world-wide demand for early detection of breast cancer at many screening sites and hospitals has resulted in the need of new research avenues. According to the World Health Organization (WHO), an early detection of cancer greatly increases the chances of taking the right decision on a successful treatment plan. The Computer-Aided Diagnosis (CAD) systems are applied widely in the detection and differential diagnosis of many different kinds of abnormalities. Therefore, improving the accuracy of a CAD system has become one of the major research areas. In this paper, a CAD scheme for detection of breast cancer has been developed using deep belief network unsupervised path followed by back propagation supervised path. The construction is back-propagation neural network with Liebenberg Marquardt learning function while weights are initialized from the deep belief network path (DBN-NN). Our technique was tested on the Wisconsin Breast Cancer Dataset (WBCD). The classifier complex gives an accuracy of 99.68% indicating promising results over previously-published studies. The proposed system provides an effective classification model for breast cancer. In addition, we examined the architecture at several train-test partitions.     

Automatic detection of coronavirus disease (COVID-19) using X-ray images and deep convolutional neural networks

The 2019 novel coronavirus disease (COVID-19), with a starting point in China, has spread rapidly among people living in other countries and is approaching approximately 101,917,147 cases worldwide according to the statistics of World Health Organization. There are a limited number of COVID-19 test kits available in hospitals due to the increasing cases daily. Therefore, it is necessary to implement an automatic detection system as a quick alternative diagnosis option to prevent COVID-19 spreading among people. In this study, five pre-trained convolutional neural network-based models (ResNet50, ResNet101, ResNet152, InceptionV3 and Inception-ResNetV2) have been proposed for the detection of coronavirus pneumonia-infected patient using chest X-ray radiographs. We have implemented three different binary classifications with four classes (COVID-19, normal (healthy), viral pneumonia and bacterial pneumonia) by using five-fold cross-validation. Considering the performance results obtained, it has been seen that the pre-trained ResNet50 model provides the highest classification performance (96.1% accuracy for Dataset-1, 99.5% accuracy for Dataset-2 and 99.7% accuracy for Dataset-3) among other four used models.

     

Computers in talk-based mental health interventions

The cost to society of mental illness is substantial. A large scale international study has identified mental illnesses as the second leading cause of disability and premature mortality in the developed world [Murray, C.L., Lopez, A.D. (Eds.), 1996. The Global Burden of Disease: A comprehensive assessment of mortality and disability from disease, injuries, and risk factors in 1990 and projected to 2020. Harvard University, Cambridge, MA]. Unfortunately, research also suggests that the majority of people suffering from treatable mental health disorders do not have access to the required treatment. Furthermore, even when treatment is accessible many sufferers are unable to successfully engage with professional services [Surgeon General, 1999. Mental Health: A Report of the Surgeon General – Executive Summary, Department of Health and Human Services, Washington, DC, Retrieved August 2006, from http://www.surgeongeneral.gov/library/mentalhealth/home.html; WHO World Mental Health Survey Consortium, 2004. Prevalence, severity, and unmet need for treatment of mental disorders in the World Health Organization World Mental Health Surveys. Journal of the American Medical Association, 291(21)]. Computer assisted mental health interventions have the potential to help in addressing this imbalance. However, a review of literature shows that to date this potential has been largely unexplored. One of the primary reasons for this is that few researchers from a HCI or technical background have engaged in this area. The primary purpose of this paper is to provide a foundation and set an agenda for future research on the design of technology for talk-based mental health interventions. Theoretical approaches to the treatment of mental illness are reviewed, as is previous research on the use of technology in this area. Several significant factors effecting design and evaluation are identified and based on these factors a broad set of design guidelines are proposed to aid the development of new technologies. Of the issues identified, ethical requirements along with the sensitivity and stigma associated with mental illness pose particular challenges to HCI professionals. These factors place strict limitations on access to mental health care (MHC) settings by non-MHC professionals and create difficulties for the direct application of traditional HCI methods, such as participatory, user-centred and iterative design. To overcome these difficulties this paper proposes a model for collaborative design and evaluation, involving both HCI and MHC professionals. The development of adaptable technologies is an important element of the proposed approach. The final contribution of the paper is to suggest future research directions and identify ways in which HCI researchers can contribute to this work.     

A two-stage classification model integrating feature fusion for coronary artery disease detection and classification

Multimedia Tools and Applications - According to the World Health Organization, Coronary Artery Disease (CAD) is a leading cause of death globally. CAD is categorized into three types, namely...     

Medical wireless IoT system and nursing intervention of chronic bronchitis based on clinical data

According to the WHO (World Health Organization), the chronic obstructive pulmonary disease has become the third leading cause of death in the group. The associated health sector related to 2030 based on the curve and the long-term cost of medical expenses. Age is the leading cause of a significant occurrence. The management of stable COPD patients to mitigate the attack, it is based on his system. Chronic obstructive pulmonary disease management system, portable spirometer and things mobile applications handheld, is a clinical information database. The Internet has established the configuration. The database, one of the MBOX format, through the Zhongshan hospital, collects data from the branch of the largest medical service provider in the region, located in the stable state of the 11 pieces of data from the main hospital of the patient follow-up. Lung function data is tested via a handheld portable spirometer and can be uploaded in real time. Other smartphone applications have been developed to establish direct communication between patients and physicians to improve disease management. It is worth noting that it classifies patient clinical data, sends it, stores it anonymously, and encrypts it for protection and privacy. What is useful in treating chronic diseases based on remote monitoring is that it significantly provides health information such as medical and biosensor data volume management and big data technology, this vast new medical technology. It can be increased. The application that needs to be processed. A potential solution for new services based on the Internet of Things is used to create the concept of big data processing.