Since the past decade, the deep learning techniques are widely used in research. The objective of various applications is achieved using these techniques. The deep learning technique in the medical field helps to find medicines and diagnosis of diseases. The Alzheimer’s is a physical brain disease, on which recently many research are experimented to develop an efficient model that diagnoses the early stages of Alzheimer’s disease. In this paper, a Hybrid model is proposed, which is a combination of VGG19 with additional layers, and a CNN deep learning model for detecting and classifying the different stages of Alzheimer’s and the performance is compared with the CNN model. The Magnetic Resonance Images are used to analyse both models received from the Kaggle dataset. The result shows that the Hybrid model works efficiently in detecting and classifying the different stages of Alzheimer’s.
Although there have been many advances in the medical field regarding disease control and management, it has been demonstrated that certain diseases and infections still represent a significant challenge. For example, the presence of oral biofilms indicates the virulence of the underlying infection in different dental infections diseases, including peri-implantitis, periapical periodontitis, periodontitis, gingivitis, and dental caries. We have discussed various mechanical, chemical, and biological modalities that can be applied to control biofilms and limit plaque formation and secondary caries. Although physical brushing might be efficacious in cleaning, evidence indicates that it cannot eradicate the underlying bacteria. Accordingly, using biological and chemical materials is essential to achieve adequate disinfection and enhance the outcomes. Many modalities have been proposed in the literature, such as nanomaterials, organic compounds such as arginine, dietary substances, and the various chemical oral cleansers discussed in the current study. Bacteriophages are also promising in this context. However, they need further exploration regarding their efficacy and safety. In addition, resistance against these compounds is a serious issue and needs to be addressed in future research.
Nanoparticles (NPs) have attracted a lot of attention in the fields of electronics, biology, and astronautics because of their unique physicochemical and electrical characteristics. NPs are materials with at least one dimension of fewer than 100 nanometres that are commercially manufactured (Bahadar et al., 2016; Vishwakarma et al., 2010). In the medical field, drugs, proteins, DNA, and monoclonal antibodies are all being delivered via NPs(Hussain et al., 2021).
Melanoma is currently known as one of the most aggressive malignant tumors. The prognostic factors and particularities of this neoplasm are a persistent hot topic in the medical field. This review has multiple purposes. First, we aim to summarize the known data regarding the histological and immunohistochemical appearance of this versatile tumor and to look further into the analysis of several widely used prognostic markers, such as B-Raf proto-oncogene, serine/threonine kinase BRAF. The second purpose is to analyze the data on the new prognostic markers, V-domain Immunoglobulin Suppressor of T cell Activation (VISTA) and Programmed death-ligand 1 (PD-L1). VISTA is a novel target that is considered to be highly important in determining the invasive potential and treatment response of a melanoma, and there are currently only a limited number of studies describing its role. PD-L1 is a marker with whose importance has been revealed in multiple types of malignancies, but its exact role regarding melanoma remains under investigation. In conclusion, the gathered data highlights the importance of correlations between these markers toward providing patients with a better outcome.
Since the Prime Minister signed the decision approving the project 32/2010 on March, 25, 2010 “Development of Social Work Profession in Vietnam in the period of 2010 – 2020”, social work has strongly developed in many different areas including the medical field. Specifically, the Ministry of Health approved the project “Development of the Social Work profession in the health sector from 2011 through 2020” and the Circular 43/2015 on “The social work activities in the hospitals and the organizational form to perform the social work activities in hospitals”. These documents are fundamental to carry out social work activities in hospitals. However, this is a new field so the implementation of these activities is still difficult and lacks detailed supporting guidelines. This study was conducted to assess the status of the five main activities of social workers in hospitals regulated under Circular 43/2015, thereby providing solutions to improve the effectiveness of social work activities.
The Grand Convalescene event was divided into handbook socialization and webinars with "Kesiapan Pendidikan Era New Normal" as the theme. The purpose of this event is to provide education, especially in the medical field, about preventing the spread of the COVID-19 and to prepare teachers and their staff to face the New Normal era. The method used in the community service activities was an online webinars, in the form of socialization and evaluation using questionnaire. The results of the analysis carried out using the questionnaire showed that by holding this event, participants became more aware of all forms of information that could help them understand COVID-19 prevention and preparation for the New Normal era. The conclusions of this activity were socialization activities provide benefits for the community and teachers in understanding the conditions in the New Normal era, then this activity helps the community and teachers to prepare themselves for facing the New Normal era, and finally helps the community and teachers in understand the prevention of COVID-19.
In the growing world of technology, where everything is available in just one click, the user expectations has increased with time. In the era of Search Engines, where Google, Yahoo are providing the facility to search through text and voice and image , it has become a complex work to handle all the operations and lot more of data storage is needed. It is also a time consuming process. In the proposed Image retrieval Search Engine, the user enters the queried image and that image is being matched with the template images . The proposed approach takes the input image with 15% accuracy to 100% accuracy to retrieve the intended image by the user. But it is found that due to the efficiency of the applied algorithm, in all cases, the retrieved images are with the same accuracy irrespective of the input query image accuracy. This implementation is very much useful in the fields of forensic, defense and diagnostics system in medical field etc. .
Machine Learning is an application of Artificial Intelligence where the method begins with observations on data. In the medical field, it is very important to make a correct decision within less time while treating a patient. Here ML techniques play a major role in predicting the disease by considering the vast amount of data that is produced by the healthcare field. In India, heart disease is the major cause of death. According to WHO, it can predict and prevent stroke by timely actions. In this paper, the study is useful to predict cardiovascular disease with better accuracy by applying ML techniques like Decision Tree and Naïve Bayes and also with the help of risk factors. The dataset that we considered is the Heart Failure Dataset which consists of 13 attributes. In the process of analyzing the performance of techniques, the collected data should be pre-processed. Later, it should follow by feature selection and reduction.