2022
DOI: 10.1142/s1793962322410069
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Blockchain with deep learning-enabled secure healthcare data transmission and diagnostic model

Abstract: At these times, internet of things (IoT) technologies have become ubiquitous in the healthcare sector. Because of the increasing needs of IoT, massive quantity of patient data is being gathered and is utilized for diagnostic purposes. The recent developments of artificial intelligence (AI) and deep learning (DL) models are commonly employed to accurately identify the diseases in real-time scenarios. Despite the benefits, security, energy constraining, insufficient training data are the major issues which need … Show more

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Cited by 69 publications
(19 citation statements)
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“…Moreover, they utilized a sorting approach for the formation of a clustering topology model to resolve the problems. Next, they proposed an energy-balanced routing method between clusters [37,38].…”
Section: Literature Reviewmentioning
confidence: 99%
“…Moreover, they utilized a sorting approach for the formation of a clustering topology model to resolve the problems. Next, they proposed an energy-balanced routing method between clusters [37,38].…”
Section: Literature Reviewmentioning
confidence: 99%
“…Table 2 and Figure 3 report a brief network lifetime (NLT) examination of the IMD-EACBR model compared to other approaches. The results indicate that the IMD-EACBR model accomplished a maximum NLT under all SNs [58][59][60][61][62][63][64][65][66][67]. For instance, with 100 SNs, the IMD-EACBR model attained an maximum NLT of 1719 rounds, whereas the sunflower optimisation (SFO), gray wolf optimisation (GWO), genetic algorithm (GA), ant line optimisation (ALO), and particle swarm optimisation (PSO) models obtained reduced NLTs of 1593, 1448, 1415, 1349, and 1300 rounds, respectively.…”
Section: Experimental Validationmentioning
confidence: 99%
“…In recent times, researchers have included new technologies along with blockchain to secure the health system. Neelakandan et al used deep learning with blockchain to secure the healthcare and diagnostic model [31]. Kamalraj et al applied an interpretable filter-based convolutional neural network in the healthcare system for glucose prediction and further analysis [32].…”
Section: Decentralised Identity Modelmentioning
confidence: 99%