RESUMEN
The development of cloud technology is a fundamental idea for offering unfettered access to many different sources in the planning of the networking, memory, infrastructure, and software. Computers are becoming more and more widespread across a wide range of industries due to their numerous advantages, notably in the healthcare industry. Typically, it is essential to the interchange of health information. In light of the ongoing issues with password security, sending private medical information via the internet still raises serious privacy concerns. Whether or whether they have complete permission, patients are not forced to divulge any of their private or personal information. This article examines several noteworthy recent studies that address the problems of password security and data privacy for cloud-based health services. These compare the benefits and drawbacks of different physical access preservation techniques. The paper also proposes a combined authentication procedure based on RFDE models. Cloud security is usually greatly hampered by the necessity for information privacy in an effort to protect sensitive and non-sensitive data for decision-making and to solve the problem of information leakage. One of the most challenging parts of the transfer of personal health records (PHRs) to the cloud is the reuse and exchange of accurate, complete medical evidence. When PHRs are outsourced to third-party businesses, such as cloud services, they are often used as patient-centered, private ways of exchanging health information. Data about a particular PHR doctor is coded for protection before being sent to the cloud. However, there are still substantial barriers due to issues with security, things that can be improved, lawful consumer privacy portfolio management, efficiency, and regulation over sensitive and non-sensitive data kept in the cloud. The PHR file may be encrypted using the Rail Fence Data Encryption (RFDE) technique to provide strong confidentiality rules and enable PHR and modular connectivity control to perform at their very best. Unauthorized users are managed to stop from accessing information with the aid of the transposition cypher, also used by RFDE and known as 'zigzag encryption.' The recommended technique generates the secret key while encrypting the PHR information. The recipient decrypts the PHR data using the private key. The algorithm works brilliantly in comparison to the prior strategy. © 2022 IEEE.
RESUMEN
Artificial Intelligence (AI) is a system that helps machines to march with human abilities within daily lifestyles. Deep learning supported by AI can be an effective application within healthcare sector. This research has explained various aspects of Deep learning application that can be a major area of concern for pushing the development process of Indian medical sector that have lack of infrastructure and lack of capacity, to take less time to optimise the medical diagnosis process. This research has also investigated the advantages and disadvantages that medical sector might face while using deep learning applications. Deep learning applications under AI systems are used to classify objects. CNN model, Machine-learning tools, and other tools that use deep learning approach are effective to diagnose any disease and in medical image analysis process. Deep learning techniques are also used to detect heart disease and manage the data regarding the patients of heart diseases. Secondary data collection method has been used and a thematic analysis has been conducted in this research to describe and find various challenges that might have been engaged within deep learning process used in medical sectors of India. It has been found that, Deep Learning is used widely for COVID-19 medical image processing through a fully connected CNN model. As a result, the main finding states that deep learning application creates a major scope for the improvement in Indian medical sector. © 2022 IEEE.