The objective of the paper is to present the techniques of Artificial Intelligence based on deep learning that can be applied to detect fractures in bones on X-rays. The paper comprises of... Show More

The objective of the paper is to present the techniques of Artificial Intelligence based on deep learning that can be applied to detect fractures in bones on X-rays. The paper comprises of discussions of various entities. Initially, there is a discussion on data formulation and processing. Following which, distinguished image processing techniques are presented for fracture detection. Later, there is an analysis of conventional and current neural network methodologies for fracture detection techniques. Furthermore, there is a comparative analysis for the same. Finally, in the end, a discussion is presented in the paper regarding problems and challenges confronted by researchers for fracture detection. The study shows, deep learning techniques provide accuracy in the diagnosis than the conventional methods in fracture detection on X-rays. The paper leads to a path for the researchers to deal with difficulties and issues encountered with the fracture detection on X-rays while using deep learning techniques.

Diabetes is a rapidly spreading disease. When the pancreas produces insufficient insulin or the body cannot utilise it effectively. Diabetic Retinopathy (DR) and blindness are two major issues for... Show More

Diabetes is a rapidly spreading disease. When the pancreas produces insufficient insulin or the body cannot utilise it effectively. Diabetic Retinopathy (DR) and blindness are two major issues for diabetics. Diabetes patients increase the amount of data collected about DR. To extract important information and undiscovered knowledge from data, data mining techniques are required. DM is necessary in DR to improve society's health. Our study focuses on the early detection of Diabetic Retinopathy using patient information. DM approaches are used to extract information from these numeric records. The dataset was used to forecast DR using logistic regression, KNN, SVM, bagged tree, and boosted tree classifiers. Two cross-validations are used to find the best features and avoid overfitting. Our dataset includes 900 diabetes patients. The boosted tree produced the best classification accuracy (90.1%) with 10% hold-out validation. KNN also achieved 88.9% accuracy, which is impressive. As a result, our research suggests that bagged trees and KNN are good classifiers for DR.

Healthcare systems around the world are beset by problems due to the lack of effective communication. Significant problems relating to patient medical records access, transition, and storage have... Show More

Healthcare systems around the world are beset by problems due to the lack of effective communication. Significant problems relating to patient medical records access, transition, and storage have persisted due to the lack of resources to adequately interact and track records between all main participants. To overcome this challenge, a nationwide Electronic Health Record (EHR) solution may be utilized. To further enhance EHR efficiency, Blockchain technology can be used to improve security, performance, and cost. In this survey, various literature proposing Blockchain-based EHR systems are discussed, along with their benefits and potential research gaps. Also Authors proposed a comprehensive architecture that could bridge all the gaps.

A novel coronavirus named COVID-19 has spread speedily and has triggered a worldwide outbreak of respiratory illness. Early diagnosis is always crucial for pandemic control. Compared to RT-PCR... Show More

A novel coronavirus named COVID-19 has spread speedily and has triggered a worldwide outbreak of respiratory illness. Early diagnosis is always crucial for pandemic control. Compared to RT-PCR, chest computed tomography (CT) imaging is the more consistent, concrete, and prompt method to identify COVID-19 patients. For clinical diagnostics, the information received from computed tomography scans is critical. So there is a need to develop an image analysis technique for detecting viral epidemics from computed tomography scan pictures. Using DenseNet, ResNet, CapsNet, and 3D-ConvNet, four deep machine learning-based architectures have been proposed for COVID-19 diagnosis from chest computed tomography scans. From the experimental results, it is found that all the architectures are providing effective accuracy, of which the COVID-DNet model has reached the highest accuracy of 99%. Proposed architectures are accessible at https://github.com/shamiktiwari/CTscanCovi19 can be utilized to support radiologists and reserachers in validating their initial screening.

Medical tourism attracts medical vacationers by promoting its uniform vacation ease, healthcare know-how, proficiency, and comprehensible amenities. With the upsurge in COVID-19 cases and no... Show More

Medical tourism attracts medical vacationers by promoting its uniform vacation ease, healthcare know-how, proficiency, and comprehensible amenities. With the upsurge in COVID-19 cases and no therapeutic treatment, non-pharmaceutical intrusions are the utmost priority. Unprecedented travel limitations and homestay restrictions are posing a huge economic burden to the tourism industry. The present study aims to identify determinants inciting sustainable e-medical tourism post COVID-19 pandemic. The study is advanced from the theoretical outlook, systematically determining and scrutinizing the prior literature to discuss the determinants which encourage e-medical tourism. The results of the study highlight that resource and management assistance, electronic supporting facilities, demand issues, technological intervention, and situational glitches act as major aspects of perseverance of e-medical tourism. An apparent limitation of the present study is the absence of contributions based on empirical data.

Machine learning (ML) has been instrumental in optimal decision making through relevant historical data, including the domain of bioinformatics. In bioinformatics classification of natural genes and... Show More

Machine learning (ML) has been instrumental in optimal decision making through relevant historical data, including the domain of bioinformatics. In bioinformatics classification of natural genes and the genes that are infected by disease called invalid gene is a very complex task. In order to find the applicability of a fresh protein through genomic research, DNA sequences need to be classified. The current work identifies classes of DNA sequence using machine learning algorithm. These classes are basically dependent on the sequence of nucleotides. With a fractional mutation in sequence, there is a corresponding change in the class. Each numeric instance representing a class is linked to a gene family including G protein coupled receptors, tyrosine kinase, synthase, etc. In this paper, the authors applied the classification algorithm on three types of datasets to identify which gene class they belong to. They converted sequences into substrings with a defined length. That ‘k value' defines the length of substring which is one of the ways to analyze the sequence.