Modern diagnostic imaging classifications and risk factors for 6G-enabled smart health systems
DOI:
https://doi.org/10.3103/S0735272723060031Keywords:
Beyond-5G networks, healthcare, IMoT, arithmetic optimization, deep learning, feature selectionAbstract
The creation of Smart Healthcare Systems is a viable strategy to improve the quality and availability of healthcare services. Identity theft, data breaches, and denial-of-service attacks are just some of the security concerns that have arisen as a result of connecting wireless networks and smart medical equipment. A secure and trustworthy smart healthcare system that can protect patient data and preserve the confidentiality of private medical information is especially important in light of these vulnerabilities. Medical diagnosis assumes increasing importance as the amount of data created on a daily basis in the 6G-enabled IoMT grows exponentially. In order to enhance anticipation accuracy and supply a real-time medicinal-diagnosis, this research presents a approach integrated into the 6G-enabled IoMT. To do this, the proposed system combines deep learning with optimization methods. MobileNetV3 architecture is then used to learn the features taken from each image. In addition, we improved the performance of the HGS-based arithmetic-optimization-algorithm (AOA). The operators of the HGS are used in the new approach, dubbed AOAHG, to improve the AOA's operation capacity as the viable province is divided up. The created AOAHG prioritizes the most important features and guarantees an overall upgrade in model categorization. When compared to other methodologies in the literature, the framework's results were impressive. The created AOAHG also outperformed alternative FS methods in terms of the achieved accuracy (Acc), precision (P), recall (R), and F1-score (F1). For instance, AOAHG had 92.12% accuracy with the ISIC dataset, 98.27% with the PH2 dataset, 95.24% with the WBC dataset, and 99.84% with the OCT dataset.
References
Abd Elaziz, M., Mabrouk, A., Dahou, A. and Chelloug, S.A., 2022. Medical image classification utilizing ensemble learning and levy flight-based honey badger algorithm on 6G-enabled internet of things. Computational Intelligence and Neuroscience, 2022.
Deebak, B.D. and Al-Turjman, F., 2023. EEI-IoT: Edge-Enabled Intelligent IoT Framework for Early Detection of COVID-19 Threats. Sensors, 23(6), p.2995.
Shaker Reddy, P.C. and Sucharitha, Y., 2023. A Design and Challenges in Energy Optimizing CR-Wireless Sensor Networks. Recent Advances in Computer Science and Communications (Formerly: Recent Patents on Computer Science), 16(5), pp.82-92.
Khattak, S.B.A., Nasralla, M.M. and Rehman, I.U., 2022, September. The Role of 6G Networks in Enabling Future Smart Health Services and Applications. In 2022 IEEE International Smart Cities Conference (ISC2) (pp. 1-7). IEEE.
Ali, M., Naeem, F., Tariq, M. and Kaddoum, G., 2022. Federated learning for privacy preservation in smart healthcare systems: A comprehensive survey. IEEE journal of biomedical and health informatics, 27(2), pp.778-789.
Lokesh, S., Priya, A., Sakhare, D.T., Devi, R.M., Sahu, D.N. and Reddy, P.C.S., 2022. CNN based deep learning methods for precise analysis of cardiac arrhythmias. International journal of health sciences, 6.
Chaudhary, S., Kakkar, R., Jadav, N.K., Nair, A., Gupta, R., Tanwar, S., Agrawal, S., Alshehri, M.D., Sharma, R., Sharma, G. and Davidson, I.E., 2022. A taxonomy on smart healthcare technologies: Security framework, case study, and future directions. Journal of Sensors, 2022.
Muthappa, K.A., Nisha, A.S.A., Shastri, R., Avasthi, V. and Reddy, P.C.S., 2023. Design of high-speed, low-power non-volatile master slave flip flop (NVMSFF) for memory registers designs. Applied Nanoscience, pp.1-10.
Sirohi, D., Kumar, N., Rana, P.S., Tanwar, S., Iqbal, R. and Hijjii, M., 2023. Federated learning for 6G-enabled secure communication systems: a comprehensive survey. Artificial Intelligence Review, pp.1-93.
Prasath, A.S.S., Lokesh, S., Krishnakumar, N.J., Vandarkuzhali, T., Sahu, D.N. and Reddy, P.C.S., 2022. Classification of EEG signals using machine learning and deep learning techniques. International journal of health sciences, 2022, pp.10794-10807.
Kumar, G.R., Reddy, R.V., Jayarathna, M., Pughazendi, N., Vidyullatha, S. and Reddy, P.C.S., 2023, May. Web application based Diabetes prediction using Machine Learning. In 2023 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI) (pp. 1-7). IEEE.
Letafati, M., Behroozi, H., Khalaj, B.H. and Jorswieck, E.A., 2022, June. Wireless-powered cooperative key generation for e-health: a reservoir learning approach. In 2022 IEEE 95th Vehicular Technology Conference:(VTC2022-Spring) (pp. 1-7). IEEE.
Javed, A.R., Ahmed, W., Pandya, S., Maddikunta, P.K.R., Alazab, M. and Gadekallu, T.R., 2023. A survey of explainable artificial intelligence for smart cities. Electronics, 12(4), p.1020.
Sucharitha, Y. and Shaker Reddy, P.C., 2022. An autonomous adaptive enhancement method based on learning to optimize heterogeneous network selection. International Journal of Sensors Wireless Communications and Control, 12(7), pp.495-509.
Shah, K., Chadotra, S., Tanwar, S., Gupta, R. and Kumar, N., 2022. Blockchain for IoV in 6G environment: Review solutions and challenges. Cluster Computing, 25(3), pp.1927-1955.
Shanmugaraja, P., Bhardwaj, M., Mehbodniya, A., VALI, S. and Reddy, P.C.S., 2023. An Efficient Clustered M-path Sinkhole Attack Detection (MSAD) Algorithm for Wireless Sensor Networks. Adhoc & Sensor Wireless Networks, 55.
Su, Z., McDonnell, D., Bentley, B.L., He, J., Shi, F., Cheshmehzangi, A., Ahmad, J. and Jia, P., 2021. Addressing Biodisaster X threats with artificial intelligence and 6G technologies: literature review and critical insights. Journal of medical Internet research, 23(5), p.e26109.
Hegde, P. and Maddikunta, P.K.R., 2023. Amalgamation of Blockchain with resource-constrained IoT devices for Healthcare applications–State of Art, Challenges and Future Directions. International Journal of Cognitive Computing in Engineering.
Srivastava, G., Jhaveri, R.H., Bhattacharya, S., Pandya, S., Maddikunta, P.K.R., Yenduri, G., Hall, J.G., Alazab, M. and Gadekallu, T.R., 2022. XAI for cybersecurity: state of the art, challenges, open issues and future directions. arXiv preprint arXiv:2206.03585.
Kharche, S. and Kharche, J., 2023. 6G Intelligent Healthcare Framework: A Review on Role of Technologies, Challenges and Future Directions. Journal of Mobile Multimedia, 19(3), pp.603-644.
Shahraki, A., Abbasi, M., Piran, M.J. and Taherkordi, A., 2021. A comprehensive survey on 6G networks: Applications, core services, enabling technologies, and future challenges. arXiv preprint arXiv:2101.12475.
Meena, P., Pal, M.B., Jain, P.K. and Pamula, R., 2022. 6G communication networks: introduction, vision, challenges, and future directions. Wireless Personal Communications, 125(2), pp.1097-1123.
Singh, P.R., Singh, V., Yadav, R. and Chaurasia, S., 2023. 6G Networks for Artificial Intelligence-Enabled Smart Cities Applications: A Scoping Review. Telematics and Informatics Reports, p.100044.
Kazmi, S.H.A., Hassan, R., Qamar, F., Nisar, K. and Ibrahim, A.A.A., 2023. Security Concepts in Emerging 6G Communication: Threats, Countermeasures, Authentication Techniques and Research Directions. Symmetry, 15(6), p.1147.
Li, D., Luo, Z. and Cao, B., 2022. Blockchain-based federated learning methodologies in smart environments. Cluster Computing, 25(4), pp.2585-2599.
