There is a massive transformation in the traditional healthcare system from the specialist-centric approach to the patient-centric approach by adopting modern and intelligent healthcare solutions to build a smart healthcare system. It permits patients to directly share their medical data with the specialist for remote diagnosis without any human intervention. Furthermore, the remote monitoring of patients utilizing wearable sensors, Internet of Things (IoT) technologies, and artificial intelligence (AI) has made the treatment readily accessible and affordable. However, the advancement also brings several security and privacy concerns that poorly maneuvered the effective performance of the smart healthcare system. An attacker can exploit the IoT infrastructure, perform an adversarial attack on AI models, and proliferate resource starvation attacks in smart healthcare system. To overcome the aforementioned issues, in this survey, we extensively reviewed and created a comprehensive taxonomy of various smart healthcare technologies such as wearable devices, digital healthcare, and body area networks (BANs), along with their security aspects and solutions for the smart healthcare system. Moreover, we propose an AI-based architecture with the 6G network interface to secure the data exchange between patients and medical practitioners. We have examined our proposed architecture with the case study based on the COVID-19 pandemic by adopting unmanned aerial vehicles (UAVs) for data exchange. The performance of the proposed architecture is evaluated using various machine learning (ML) classification algorithms such as random forest (RF), naive Bayes (NB), logistic regression (LR), linear discriminant analysis (LDA), and perceptron. The RF classification algorithm outperforms the conventional algorithms in terms of accuracy, i.e., 98%. Finally, we present open issues and research challenges associated with smart healthcare technologies.

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A Taxonomy on Smart Healthcare Technologies: Security Framework, Case Study, and Future Directions

n recent years it has been shown that the secure exchange of medical information significantly benefits people’s life quality, improving their care and treatment. The interoperability of the entire healthcare ecosystem is a constant challenge, and even more, with all the risks posed to the security of healthcare information. Blockchain technology is emerging as one of the main alternatives when it comes to finding a balance in the healthcare ecosystem. However, the constant development of new Blockchain technologies and the evolution of healthcare systems make it difficult to find established proposals. From an architectural point of view, the design of blockchain-based solutions requires trade-offs e.g., security and interoperability. This paper focuses on two main objectives, in the first one, it was carried out a Systematic Literature Review for exploring architectural mechanisms used to support the interoperability and security of Blockchain-based Health Management Systems. Taking into account of results, a series of scenarios were generated where these mechanisms can be used along with their context, issues, and various architectural concerns (interoperability and security). In the second objective, a high-level architecture and its validation were proposed through an experiment for the whole process of developing a Domain Specific Language, using the Model Driven Engineering methodology for specific Smart Contracts.

https://ieeexplore.ieee.org/ielx7/6287639/6514899/10015729.pdf

Blockchain for Healthcare Management Systems: A Survey on Interoperability and Security

Modern ages have perceived an exemplar swing in warehousing electronic health records (EHRs) on cloud settings with the catholic solicitation of mobile healthcare systems, where mobile devices are assimilated by means of cloud computing to assist medicinal data interactions amid patients and surgeons. The distribution of EHRs has prodigious optimistic connotation for investigation of disease and doctors’ diagnosis. This radical model empowers healthcare amenities with truncated operating charge, high flexibility, as well as EHRs accessibility. It is of paramount significance to hitch therapeutic evidence sprinkled through healthcare establishments to provision exhaustive data investigation and accomplish custom-made healthcare. Nevertheless, this newfangled archetype also nurtures apprehensions about data privacy in addition to network security for healthcare schemes. Exactly how to steadfastly stake data among mobile consumers while pledging sophisticated security echelons in cloud is a perplexing concern. Blockchain, by means of a public ledger pigeonholed through its transparency, tamper-evidence, decentralization, unforgeability, verifiability, as well as anonymity can support in forming a secure healthcare scheme. Existing works primarily recompense devotion to centralized in addition to Blockchain-based mechanisms. Nonetheless, they could not acclimatize to the snowballing volume of global medical records and agonize from multifaceted application encounters, singly. In this paper, the authors recommend a Diagonal Digital Signature Algorithm (DDSA) with Merkle Patricia Hash Trie (MPHT) algorithm which is an innovative EHRs sharing framework that exploits Blockchain to expedite secure medical record sharing amid dissimilar patients and surgeons. The pragmatic outcomes illustrate that our scheme affords an operative elucidation for unswerving data interactions on cloud while conserving delicate medical records in contrast to impending intimidations. This work completely works for the Architecture of data can be shared in secured way in the IoT-based health care applications. In the proposed secure data sharing scheme for smart medical applications, the amount of exponential and bilinear mapping procedures in the course of decryption is habitually proportional to the complexity of access control policy. The scheme assessment along with the security investigation also exhibit the performance enhancements in light-weight access control; slightest network latency, transmission delay and file loss rate; with extraordinary security as well as data privacy, paralleled to the prevailing record sharing schemes.

Securing IoT-based healthcare systems from counterfeit medicine penetration using Blockchain

In recent years, deep learning has been applied to many medical imaging fields, including medical image processing, bioinformatics, medical image classification, segmentation, and prediction tasks. Computer-aided detection systems have been widely adopted in brain tumor classification, prediction, detection, diagnosis, and segmentation tasks. This work proposes a novel model that combines the Bayesian algorithm with depth-wise separable convolutions for accurate classification and predictions of brain tumors. We combine Bayesian modeling learning and Convolutional Neural Network learning methods for accurate prediction results to provide the radiologists the means to classify the Magnetic Resonance Imaging (MRI) images rapidly. After thorough experimental analysis, our proposed model outperforms other state-of-the-art models in terms of validation accuracy, training accuracy, F1-score, recall, and precision. Our model obtained high performances of 99.03% training accuracy and 94.32% validation accuracy, F1-score, precision, and recall values of 0.94, 0.95, and 0.94, respectively. To the best of our knowledge, the proposed work is the first neural network model that combines the hybrid effect of depth-wise separable convolutions with the Bayesian algorithm using encoders.

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Bayesian Depth-Wise Convolutional Neural Network Design for Brain Tumor MRI Classification

The rapid and dangerous spread of COVID-19 has forced governments in various countries to provide information on patients’ medical records to the public in the context of prevention. Meanwhile, patients’ medical records are vital and confidential because they contain patients’ privacy. Changing and falsifying a patient’s medical record leads to various dangerous consequences, such as mishandling which results in the patient’s death. From these problems, the research introduces a new model with a combination of blockchain technology and the Elliptic Curve Digital Signature Algorithm (ECDSA) to secure the medical records of COVID-19 patients. This model is an improvement from the model and framework proposed by previous researchers. The proposed model consists of two big parts (front and back end). Then, the simulations are carried out to measure and prove the level of security of blockchain technology in securing patient medical records. The research results show that the ECDSA algorithm can protect patients’ medical records from being opened by unauthorized parties. Then, blockchain technology can prevent changes or manipulation of patient medical records because the information recorded on the blockchain network is impossible to change and will be immutable. The research has successfully introduced a new model in securing patient medical records.

Securing Medical Records of COVID-19 Patients Using Elliptic Curve Digital Signature Algorithm (ECDSA) in Blockchain

The common issues in medical services within the country are mostly associated with doctors' referral process, data transfer between health institutions, and portals for patients to access their medical information. Specific issues arise, such as sharing health Records across institutes or hospitals, problems with misuse of data once shared, no security, etc. The Electronic Health Record (EHR) Framework on Blockchain addresses those issues, resulting from a collaboration of all stakeholders involved. This paper explores the likelihood of representing medical records to make sure data privacy, data accessibility, and data interoperability for the healthcare-specific scenario. Data privacy refers to affording protection to ensure data is available when needed and not used, imparted, accessed, altered, or deleted while being stored or retrieved, or transmitted. Data accessibility is the ability to access the data regardless of natural or artificial accidents, hardware, or others. Improving the accessibility of health data in the healthcare sector while ensuring privacy has been identified as a necessary capability that involves every individual and organization. Traditionally, healthcare interoperability has centered on sharing data between business institutions, such as various hospital systems. The emphasis has lately been on patient-driven information sharing, where the exchange of medical information is patient-mediated and patient-driven. We propose implementing a large-scale information infrastructure to access Smart Contracts sponsored by EHRs as information mediators. The decentralized nature of blockchain technology will aid in making the EHR accessible over a broader network. Using Blockchain will help make far-reaching changes in the healthcare industry by providing immutable, authentic, and accessible medical records, privacy, and faster payments.

A Blockchain based Electronic Medical Health Records Framework using Smart Contracts

Government funded projects require the use of bidding via tenders, this process is tedious and leads to corruption as there is very less transparency. This paper proposes a solution using Blockchain as a Service for easy and transparent administration of public projects which allows various stakeholders to scrutinize the entire process.

BlockchainAs a Service (BaaS) Framework for Government Funded Projects e-Tendering Process Administration and Quality Assurance using Smart Contracts

Farmer Suicides are a major issue in India due to various factors. One of the major concerns being the financial aspect of managing and growing the crops while still trying to turn a profit. This paper proposes a decentralised platform for buying and selling of agriculture produce by connecting farmers with individuals interested in investing in their fields with the continuous monitoring of the quality and crop health using IOT and Machine Learning for predicting diseases in the agricultural produce.

IoT and Machine Learning based Peer to Peer Platform for Crop Growth and Disease Monitoring System using Blockchain

A Machine Learning based software is developed using brain magnetic resonance which is used for segmentation and to analyze if the tumor is benign and malignant. In this paper, contrast improvement procedure, twofold thresholding is finished utilizing morphological tasks to upgrade the MRI picture and to eliminate undesirable non cerebral tissues from MR picture a cycle called the skull stripping is utilized most. This is done to fragment the cerebrum tumor and it is finished by utilizing a cut of Magnetic Resonance (MR). Picture Feature are determined utilizing GLCM is utilized to infer the picture highlights and precise division of the tumor district is done in less calculation time. This is finished via preparing these highlights in a specific strategy. Subsequently, in the proposed framework, utilizing triple procedure K-means, KNN and FCM highlights that are gotten from the GLCM, can be more precise and proficient. This Is finished utilizing triple method Means, FCM, and KNN so the exactness and blunder rate is determined for cerebrum MRI picture.

MRI Image based Brain Tumour Segmentation using Machine Learning Classifiers

Fetal Health denotes the health and growth of the fetal and frequent contacts in the uterus of the pregnant women during pregnancy. Maximum pregnancy period complexities leads fetal to a severe difficulty which limits right growth that causes deficiency or death. Harmless pregnancy period by predicting the risk levels before the occasion of difficulties boost right fetal growth. Forecasting the fetal health and growth state from a set of pre-classified patterns knowledge is vital in developing a predictive classifier model using Machine Learning Algorithms.

R. Chinnaiyan

Papers

A Blockchain based Electronic Medical Health Records Framework using Smart Contracts

BlockchainAs a Service (BaaS) Framework for Government Funded Projects e-Tendering Process Administration and Quality Assurance using Smart Contracts

IoT and Machine Learning based Peer to Peer Platform for Crop Growth and Disease Monitoring System using Blockchain

MRI Image based Brain Tumour Segmentation using Machine Learning Classifiers

Machine Learning Approaches for Early Diagnosis and Prediction of Fetal Abnormalities