Abstract
The healthcare domain is one of the fastest-growing fields for the Internet of Things and Artificial Intelligence. The advancement of medical resources is insufficient to meet the needs of remote patient monitoring and treatment. This issue is growing increasingly prevalent in developing countries. The convergence of IoT and AI solves this problem significantly. A remote monitoring system for breast cancer patients is urgently needed in order to provide effective care to them. This study examines related research on existing and future technologies for breast cancer detection, and how the confluence of IoT and AI is leading to the emergence of smart healthcare. Various breast cancer screening approaches have been briefly addressed, as well as popular public databases. Following that, issues in remote monitoring system have been discussed. We also present a case study on remote monitoring system for breast cancer patients to provide enhanced solution for women in rural areas.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Kumar Y, Gupta S, Singla R et al (2021) A systematic review of artificial intelligence techniques in cancer prediction and diagnosis. Archi Comput Methods Eng State Art Rev 1–28
Iranpak S, Shahbahrami A, Shakeri H (2021) Remote patient monitoring and classifying using the Internet of Things platform combined with cloud computing. J Big Data 8(Article number 120) 1–22
Baker SB, Xiang W, Atkinson I (2017) IoT for smart healthcare: technologies, challenges and opportunities. IEEE Access 5:26521–26544
Liaqat M, Iqbal MJ, Javed Z et al (2021) Clinical applications of artificial intelligence and machine learning in cancer diagnosis: looking into the future. Cancer Cell Int 21(Article number 270)
Malasinhe LP, Ramzan N, Dahal K (2019) Remote patient monitoring: a comprehensive study. J Ambient Intell Humaniz Comput 10:57–76
El-Rashidy N, El-Sappagh S, Riazul Islam SM (2017) Mobile health in remote patient monitoring for chronic diseases: principles, trends, and challenges. Diagnostics 11(4)
Qadri YA, Nauman A, Zikria YB et al (2020) The future of healthcare IoT: a survey of emerging technologies. IEEE Comm Surveys Tutori 22(2):1121–1167
Alanazi SA, Kamruzzaman MM, Nazirul Md et al (2021) Boosting breast cancer detection using convolutional neural network. J Healthcare Eng 2021(Article ID 5528622), 11 p
Vakaa AR, Sonia B, Sudheer Reddy K (2020) Breast cancer detection by leveraging machine learning. Science Direct ICT Express 6(4):320–324. ISSN 2405-9595
Taleb H, Nasser A, Andrieux G et al (2021) Wireless technologies, medical applications and future challenges in WBAN: a survey. Wirel Netw 27:5271–5295
Salvi S, Kadam A (2021) Breast cancer detection using deep learning and IoT technologies. Journal of Physics: Conference Series 1831 012030. In: International conference on robotics and artificial intelligence (RoAI)
Abdelhafiz D, Yang C, Ammar R, Nabavi S (2019) Deep convolutional neural networks for mammography: advances, challenges and applications. Bio Inform 20(Article number 281)
Mashekova A, Zhao Y, Ng EYK et al (2022) Early detection of the breast cancer using infrared technology—A comprehensive review. In: Thermal science and engineering progress, vol 27. Elsevier
Mohamed EA, Rashed EA, Gaber T, Karam O (2022) Deep learning model for fully automated breast cancer detection system from thermograms. PloS Public Library Sci One 17(1):e0262349
Rehman O, Farrukh Z, Al-Busaidi AM et al (2020) IoT powered cancer observation system. SMA 2020, pp 313–318
Halim A, Andrew A, Melvin A et al (2019) Existing and emerging breast cancer detection technologies and its challenges: a review. Appl Sc 11(22):10753
Gogoi UR, Majumdar G, Bhowmik MK. Evaluating the efficiency of infrared breast thermography for early breast cancer risk prediction in asymptomatic population. Infrared Phys Technol 99,:201–211
Rajasekaran Subramanian D, Rubi D, Lakshmi RG et al (2020) Breast cancer lesion detection and classification in radiology images using deep learning. Eur J Mol Clin Med 07(3)
Hamim M, Paul S, Hoque SI et al (2019) IoT based remote health monitoring system for patients and elderly people. In: International conference on robotics, electrical and signal processing techniques (ICREST). INSPEC Accession Number 18473342
Pradhan B, Bhattacharyya S, Pal K (2021) IoT-based applications in healthcare devices. J Healthcare Eng 2021 (article ID 6632599) 18 p
Agarwal R, DÃaz O, Yap MH et al (2020) Deep learning for mass detection in full field digital mammograms. Comput Biol Med 121:103774. ISSN 0010-4825
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Parshionikar, S., Bhattacharyya, D. (2023). Remote Breast Cancer Patient Monitoring System: An Extensive Review. In: Ogudo, K.A., Saha, S.K., Bhattacharyya, D. (eds) Smart Technologies in Data Science and Communication. Lecture Notes in Networks and Systems, vol 558. Springer, Singapore. https://doi.org/10.1007/978-981-19-6880-8_12
Download citation
DOI: https://doi.org/10.1007/978-981-19-6880-8_12
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-19-6879-2
Online ISBN: 978-981-19-6880-8
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)