Image encryption with leveraging blockchain-based optimal deep learning for Secure Disease Detection and Classification in a smart healthcare environment

Fatma S. Alrayes; Latifah Almuqren; Abdullah Mohamed; Mohammed Rizwanullah; Fatma S. Alrayes; Latifah Almuqren; Abdullah Mohamed; Mohammed Rizwanullah

doi:10.3934/math.2024779

AIMS Mathematics

2024, Volume 9, Issue 6: 16093-16115. doi: 10.3934/math.2024779

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Research article Special Issues

Image encryption with leveraging blockchain-based optimal deep learning for Secure Disease Detection and Classification in a smart healthcare environment

1.
Department of Information Systems, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
2.
Research Centre, Future University in Egypt, New Cairo, 11845, Egypt
3.
Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam bin Abdulaziz University, AlKharj, Saudi Arabia

Received: 25 January 2024 Revised: 29 March 2024 Accepted: 12 April 2024 Published: 08 May 2024
MSC : 11Y40

Blockchain (BC) in healthcare can be used for sharing medical records and secure storage and other confidential data. Deep learning (DL) assists in disease recognition through image analysis, specifically in detecting medical conditions from images. Image encryption ensures the security and privacy of medical images by encrypting the image before sharing or storage. The combination of image encryption, BC, and DL provides an efficient and secure system for medical image analysis and disease detection in healthcare. Therefore, we designed a new BC with an Image Encryption-based Optimal DL for Secure Disease Detection and Classification (BIEODL-SDDC) technique. The presented BIEODL-SDDC technique enables the secure sharing of medical images via encryption and BC technology with a DL-based disease classification process. Furthermore, the medical image encryption process took place using the ElGamal Encryption technique with a giraffe kicking optimization (GKO) algorithm-based key generation process. In addition, BC-based smart contracts (SCs) were used for the secure sharing of medical images. For the disease detection process, the BIEODL-SDDC technique encompassed EfficientNet-B7-CBAM-based feature extraction, Adam optimizer, and a fully connected neural network (FCNN). The experimental validation of the BIEODL-SDDC technique was tested on medical image datasets and the outcome highlighted an enhanced accuracy outcome of 94.81% over other techniques.
- security,
- smart healthcare,
- disease detection,
- image encryption,
- deep learning,
- key generation
Citation: Fatma S. Alrayes, Latifah Almuqren, Abdullah Mohamed, Mohammed Rizwanullah. Image encryption with leveraging blockchain-based optimal deep learning for Secure Disease Detection and Classification in a smart healthcare environment[J]. AIMS Mathematics, 2024, 9(6): 16093-16115. doi: 10.3934/math.2024779

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Abstract

Blockchain (BC) in healthcare can be used for sharing medical records and secure storage and other confidential data. Deep learning (DL) assists in disease recognition through image analysis, specifically in detecting medical conditions from images. Image encryption ensures the security and privacy of medical images by encrypting the image before sharing or storage. The combination of image encryption, BC, and DL provides an efficient and secure system for medical image analysis and disease detection in healthcare. Therefore, we designed a new BC with an Image Encryption-based Optimal DL for Secure Disease Detection and Classification (BIEODL-SDDC) technique. The presented BIEODL-SDDC technique enables the secure sharing of medical images via encryption and BC technology with a DL-based disease classification process. Furthermore, the medical image encryption process took place using the ElGamal Encryption technique with a giraffe kicking optimization (GKO) algorithm-based key generation process. In addition, BC-based smart contracts (SCs) were used for the secure sharing of medical images. For the disease detection process, the BIEODL-SDDC technique encompassed EfficientNet-B7-CBAM-based feature extraction, Adam optimizer, and a fully connected neural network (FCNN). The experimental validation of the BIEODL-SDDC technique was tested on medical image datasets and the outcome highlighted an enhanced accuracy outcome of 94.81% over other techniques.

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