A Novel Image Processing Methodology for X-ray Image Compression and Enhancement

Currently, there a growing demand of data produced and stored in clinical domains. Therefore, for effective dealings of massive sets of data, a fusion methodology needs to be analyzed by considering the algorithmic complexities. For effective minimization of the severance of image content, hence minimizing the capacity to store and communicate data in optimal forms, image processing methodology has to be involved. In that case, in this research, two compression methodologies: lossy compression and lossless compression were utilized for the purpose of compressing images, which maintains the quality of images. Also, a number of sophisticated approaches to enhance the quality of the fused images have been applied. The methodologies have been assessed and various fusion findings have been presented. Lastly, performance parameters were obtained and evaluated with respect to sophisticated approaches. Structure Similarity Index Metric (SSIM), Mean Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR) are the metrics, which were utilized for the sample clinical pictures. Critical analysis of the measurement parameters shows higher efficiency compared to numerous image processing methods. This research draws understanding to these approaches and enables scientists to choose effective methodologies of a particular application.

Advantages and Functions of Clinical and Decision Support Systems

Clinical Decision Support Systems (CDSSs) signify the framework shift in the medical sector in the modern age. CDSSs are utilized in augmenting healthcare facilities in the process of making complex clinical decisions. Since the first application of CDSSs in the 80s, the framework has witnessed significant transformation. The frameworks are now administered through electronic healthcare records with complex capacities. Irrespective of these complex advancements, there are existing questions concerning the impacts of CDSSs on service providers, healthcare costs, and patients’ records. There are many published texts concerning the success stories of CDSSs, but significant setbacks have proved that CDSSs are not without any potential risks. In this research, we provide critical analysis on the application of CDSSs in clinical setting, integrating various forms, present application cases with proven effectiveness, potential harms and common pitfalls. We therefore conclude with evidence-centered recommendation for mitigating the issues of CDSSs maintainability, evaluation, implementation and designing.

Modelling for Clinical and Psysiological Evaluation of Diabetes and Glucose Homeostasis

There is a long history of arithmetic simulations in the domain of gluconeogenesis. There are various reasons why frameworks are used. Paradigms have been employed to calculate physiologically relevant parameters from intermediate experimental evidence, to offer a clear quantitative description of pathophysiology processes, and to identify clinical relevance indicators from basic empirical procedures. The creation and application of frameworks in this field has expanded in response to the rising social effect of type 2 diabetes that entails a disruption of the glycemic homeostasis system. The frameworks' emphasis has ranged from depictions of entire body functions to lymphocytes (form “in Vivo” to “in Vitro”) study, following the methodologies of physiologic and medicinal exploration. Framework-based techniques to connecting in vivo and in vitro research, and also multi-resolution systems that combine the two domains, have been presented. The arithmetic and psychological domains have had varying levels of effectiveness and influence.

The Scope, Methods and Applications of Biomedical Data Mining

Most literature assumptions have been drawn from public databases e.g. NHANES (National Health and Nutrition Examination Survey). Nonetheless, the sets of data are typically featured by high-dimensional timeliness, heterogeneity, characteristics and irregularity, hence amounting to valuation of these databases not being applied completely. Data Mining (DM) technologies have been the frontiers domains in biomedical studies, as it shows smart routine in assessing patients’ risks and aiding in the process of biomedical research and decision-making in developing disease-forecasting frameworks. In that case, DM has novel merits in biomedical Big Data (BD) studies, mostly in large-scale biomedical datasets. In this paper, a description of DM techniques alongside their fundamental practical applications will be provided. The objectives of this study are to help biomedical researchers to attain intuitive and clear appreciative of the applications of data-mining technologies on biomedical BD to enhance to creation of biomedical results, which are relevant in a biomedical setting.

A Survey of HISs with Information Systems Success Model

Hospital Information System (HIS) is a form of healthcare information system that is globalized and applied in the medical sector. Researchers, doctors, and management are all interested in the rate of success of HISs; therefore it's a continuous study topic. At this research, we created a new tool to assess the success rate of HIS in a medical center based on the perspectives of users. The research was place in Ebnesina and Mashhad, Persia, at the Dr. Hejazi Mental Center and Educational Facility. A self-administered standardized questionnaire based on Information Systems Success Model (ISSM) was used to gather data, and it included seven factors: systems quality, data quality, quality of service, system use, applicability, fulfillment, and positive externalities. An advisory group checked the content's legitimacy. Cronbach alpha was used to test the consistency and stability of dimensions. To examine the importance of relationships between variables, Correlation and regression was determined. On the basis of user feedback, the HIS rate of success has been established. The research included a approximately 125 participants. A content validity index (CVI) of 0.8 and a validity ratio (CVR) of 0.86 were used by an advisory committee to verify the item. The instruments have an overall Cronbach's alpha of 0.9. Between the analyzed dimensions, the Pearson’s correlation coefficient revealed substantial positive connections. In the institution under investigation, the HIS rate of success averaged 65 percent. (CI: 64 percent, 67 percent). The greatest success rates were found in the aspects of "effectiveness," "systems quality," and "positive externalities." Future research might employ the tool used in this research to evaluate HIS. In this research, a technique for calculating the HIS rate of success depending on user feedback was established. This strategy enables institutional HIS chances of success to be compared. Our results also highlight the perspectives of HIS clients in a developing economy.

Medical Identification and Sensing Technology for Assisting and E-Health Monitoring Systems for Disabled and Elderly Persons

This research gives an overview of numerous kinds of identification and sensor technology that have been shown to improve the standard of living of older persons in hospital and home settings. Recent advancements in semiconductors and microsystems have enabled the creation of low-cost medical equipment, which are used by various persons as prevention and E-Health Monitoring (EHM) tools. Remote health management, which relies on wearable and non-invasive sensing devices, controllers, and current information and communication technology, provides cost-effective solutions that enable individuals to remain in their familiar homes while being safeguarded. Additionally, when preventative actions are implemented at home, costly medical centers are becoming available for use by intensive care patients. Patients' vital physiological indicators may be monitored in real time by remote devices, which can also watch, analyze, and, most importantly, offer feedback on their health problems. To translate different types of vital indicators into electrical impulses, sensors are employed in computerized healthcare and non-medical devices. Life-sustaining implants, preventative interventions, and long-term E-Health Monitoring (EHM) of handicapped or unwell patients may all benefit from sensors. Whether the individual is in a clinic, hospital, or at home, medical businesses, such as health insurers, want real-time, dependable, and precise diagnostic findings from sensing devices that can be examined virtually.

Survey on Artificial Intelligence based Image Processing and Image Segmentation analysis

This paper critically surveys the aspect of digitalized image processing and segmentation with central focus on artificial intelligence. A digitalized image is composed of numerous elements that must be "analyzed," since better “phrasing”, and the "research" done on such elements might disclose a lot of strange data. This data may assist in solving a variety of business challenges, which is one of the numerous end objectives associated with image processing. Digital image analysis of image processing is a collection of methodologies applied to process computerized images. It integrates accomplishing basis assignments such noise minimization and more complex assignments such as image classification, fault diagnostics, emotional response, image fragmentation and image segmentation. Image enhancement applying neural networks has, over the past few decades, been applied widely in the clinical setting due to the advent of advanced computing ecosystems and algorithms. Medicine, industries, police departments, agriculture, defense, finance and security are some of the additional domains where the concept of image processing and segmentation can be applied.

Analysis of Biomedical Applications in Embedded Systems Devices

Embedded systems are rapidly being used in clinical and biological applications, as well as commercial, telecommunications, government, and other business applications. Embedded system solutions are growing in popularity, not only with types of technologies, garments, industries, healthcare and military hardware, and mobile computers, but with software solutions like' electronic worlds' and 'mobile worlds,' deep learning, and internet of things, which allow for the creation of a wide range of application. With the growth of viral illnesses like the Covid-19 virus, tele-health technologies for diagnostics, prognostic, and patient treatment have become more important in recent decades. In medical technologies, embedded device techniques have taken a significant role. Developing techniques to improve the security of medical practitioners in the case of pandemic contagious diseases, such as epidemics, is particularly important. Patients released from clinics home-based or in treatment wards that are non-intensive during the quarantine period, or segregated in their residences, outpatients’ departments, and moderately ailing individuals are progressively being monitored remotely, instantaneously, safely, and rapidly for this reason. The applications biomedical applications in embedded systems will be discussed in this paper.

A Survey on Biomedical Informatics for Computer-Aided Decision Support System

Biomedical computing for computer-aided biomedical diagnostics and the decision support system has developed a platform for the biomedical setting during the last few decades. As early as 1971, there were elaborate and basic applications of management information systems driven by biomedical informatics. According to a 1994 assessment, this field's literature stretches back to the 1950s. Medical decision is more challenging than ever for doctors and other caregivers due to the amount and complexity of contemporary patient information. This circumstance necessitates the application of medical computing technologies to evaluate data and formulate suggestions and/or forecasts to aid decision makers. Over the past two decades, healthcare informatics tools, such as computer-aided decision support, have grown indispensable and extensively employed. This article gives a quick overview of such technologies, their productivity applications and methodology, as well as the problems and directions they imply for the future.

The Classification of Patient Semantical Records and Medical Images

In order to alleviate suffering and pain, clinical diagnosis and therapy are critical. Medical photographs play an important role in diagnosing disorders and tracking treatment outcomes. Images have visual and semantic qualities. Texture are essential parameters, whereas form and spatial connection are geometrical elements. The meaning of a picture in an abstract representation based on phrases or informative text is known as semantic characteristics. Both qualities are used in medical diagnostics to extract properties at the micro- and macro-levels, such as distinguishing cancerous cells from standard ones. Extracting characteristics may be done in a number of ways. Computational and numerical modifications are used in these techniques. Following the extraction of the characteristics, classifications based on expertise and domain norms commence. The normalcy or irregularity of a particular picture might be used to make medical judgments. In this paper, we propose using artificial intelligence and data mining approaches to extract and categorize features for a decision - making support system that includes a comprehensive database of client semantic and syntactic records and photographs.