Blockchain with deep learning-enabled secure healthcare data transmission and diagnostic model
- Title
- Blockchain with deep learning-enabled secure healthcare data transmission and diagnostic model
- Creator
- Neelakandan S.; Rene Beulah J.; Prathiba L.; Murthy G.L.N.; Fantin Irudaya Raj E.; Arulkumar N.
- Description
- 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 to be resolved in the IoT enabled medical field. To accomplish the security, blockchain technology is recently developed which is a decentralized architecture that is widely utilized. With this motivation, this paper introduces a new blockchain with DL enabled secure medical data transmission and diagnosis (BDL-SMDTD) model. The goal of the BDL-SMDTD model is to securely transmit the medical images and diagnose the disease with maximum detection rate. The BDL-SMDTD model incorporates different stages of operations such as image acquisition, encryption, blockchain, and diagnostic process. Primarily, moth flame optimization (MFO) with elliptic curve cryptography (ECC), called MFO-ECC technique is used for the image encryption process where the optimal keys of ECC are generated using MFO algorithm. Besides, blockchain technology is utilized to store the encrypted images. Then, the diagnostic process involves histogram-based segmentation, Inception with ResNet-v2-based feature extraction, and support vector machine (SVM)-based classification. The experimental performance of the presented BDL-SMDTD technique has been validated using benchmark medical images and the resultant values highlighted the improved performance of the BDL-SMDTD technique. The proposed BDL-SMDTD model accomplished maximum classification performance with sensitivity of 96.94%, specificity of 98.36%, and accuracy of 95.29%, whereas the feature extraction is performed based on ResNet-v2 World Scientific Publishing Company.
- Source
- International Journal of Modeling, Simulation, and Scientific Computing, Vol-13, No. 4
- Date
- 2022-01-01
- Publisher
- World Scientific
- Subject
- Blockchain; disease diagnosis; encryption; healthcare; privacy; security
- Coverage
- Neelakandan S., Department of CSE R.M.K Engineering College, Chennai, India; Rene Beulah J., Department of CSE College of Engineering and Technology Faculty of Engineering and Technology SRM Institute of Science and Technology SRM Nagar, Kattankulathur, Chennai, India; Prathiba L., MIT Art Design and Technology University, Pune, India; Murthy G.L.N., Lakireddy Bali Reddy College of Engineering, Andhra Pradesh, Mylavaram, India; Fantin Irudaya Raj E., Department of EEE Dr., Sivanthi Aditanar College of Engineering, Tamilnadu, India; Arulkumar N., Department of CS CHRIST, Deemed to be University), Bangalore, India
- Rights
- Restricted Access
- Relation
- ISSN: 17939623
- Format
- Online
- Language
- English
- Type
- Article
Collection
Citation
Neelakandan S.; Rene Beulah J.; Prathiba L.; Murthy G.L.N.; Fantin Irudaya Raj E.; Arulkumar N., “Blockchain with deep learning-enabled secure healthcare data transmission and diagnostic model,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 24, 2025, https://archives.christuniversity.in/items/show/14962.