A novel chaotic-based advanced encryption standard algorithm with blockchain technology for secured IoMT data transfer and disease diagnosis

The increasing adoption of Internet of Medical Things (IoMT) devices generates vast amounts of sensor data in healthcare, posing challenges in data security, privacy and processing efficiency. This work addresses these issues by proposing a secure and efficient storage and classification system for...

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Vydané v:Automatika Ročník 66; číslo 4; s. 883 - 903
Hlavní autori: R, Deepa, N, Bharathi
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Ljubljana Taylor & Francis Ltd 02.10.2025
Taylor & Francis Group
Predmet:
adaptive multi-scale dilation-assisted residual attention network
advanced chaotic-based encryption standard
Algorithms
Blockchain
blockchain technology
Data transfer (computers)
Datasets
Encryption
Health care
Heuristic methods
Internet of medical things
Management systems
Medical imaging
Real time
renovated position-based galactic swarm with yellow saddle goatfish optimization
skin disease classification
Skin diseases
ISSN:0005-1144, 1848-3380
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Shrnutí:The increasing adoption of Internet of Medical Things (IoMT) devices generates vast amounts of sensor data in healthcare, posing challenges in data security, privacy and processing efficiency. This work addresses these issues by proposing a secure and efficient storage and classification system for skin disease detection. We present a blockchain-based healthcare storage solution where skin images are collected by IoMT devices, encrypted using the Chaotic-based Advanced Encryption Standard (CAES), and stored on a blockchain for secure, decentralized access. The encrypted images are then retrieved and classified using an Adaptive Multi-Scale Dilation-assisted Residual Attention Network (AMSD-RAN), optimized with a Hybrid Heuristic Algorithm, Renovated Position-based Galactic Swarm with Yellow Saddle Goatfish Optimization (RPGS-YSGO). It provides a scalable and secure solution for real-time healthcare applications. The proposed method shows 95% accuracy which is improved by 6.7% for Harvard Dataverse dataset and by 12% for PH2 dataset and significant reduction in processing time compared to previous approaches. This solution ensures secure storage, accurate skin disease detection and overcomes the limitations of traditional IoT and centralized management systems through blockchain’s decentralized architecture.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0005-1144
1848-3380
DOI:10.1080/00051144.2025.2570956