Secure medical data sharing and brain tumor diagnosis using optimized encryption with blockchain-enabled multiscale channel attention residual network.
Authors
Affiliations (5)
Affiliations (5)
- Department of Computer Application, JSS University Noida, Uttar Pradesh 201301, India. Electronic address: [email protected].
- Department of Computer Science and Engineering, Siddhartha Institute of Technology & Sciences (UGC -AUTONOMOUS), Narapally, Telangana, India.
- Department of Electrical and Electronics Engineering, Mangalayatan Univeristy, Aligarh, Uttar Pradesh, India.
- Department of Computer Science and Technology, TKR College of Engineering and Technology, Hyderabad, Telangana, India.
- Department of Electronics and Telecommunication Engineering, Finolex Academy of Management and Technology, Ratnagiri, Maharashtra 415639, India.
Abstract
Blockchain (BC) may transform the future of medical systems through reliable storage of medical records and controlled data access, minimizing the risk of large-scale breaches and boosting trust in information exchange. On the flip side, deep learning (DL) is set to revolutionize healthcare diagnostics by quickly and accurately identifying diseases, predicting outcomes, improving health outcomes, reducing costs, and reducing clinicians' workload. An optimized encryption with a blockchain based-deep learning approach is emphasized in this manuscript for effective data sharing and classifying brain tumor in magnetic resonance (MRI) samples. First, the raw brain MRI images are collected from a freely accessible source. Then, the triple chaotic encryption with a blockchain mechanism is introduced to encrypt and store the patients MRI samples to enable access control for different stakeholders. In the encryption process, an innovative secretary bird optimizer (SBO) technique is employed for the optimal key generation process. Following this, a Kuwahara Anisotropic diffusion filtering (KADF) technique is emphasized to enhance the image quality to remove unwanted noise. Then, the Multiscale channel attention residual network (MCA-ResNet) is proposed to detect and classify various brain tumors such as glioma, meningioma, pituitary, and normal, accurately. The developed scheme is simulated via the Python platform. Here evaluates diverse computational metrics, including accuracy, computation time (CT), cost correlation (CC), false negative rate (FNR), F-score, markedness (MK), Informedness (BM), peak signal-to-noise ratio (PSNR), and precision, are evaluated and compared with traditional frameworks. The overall accuracy of 98.3%, CT of 16 s, CC of 97.5%, FNR of 1.7, BM of 0.96, MK of 0.97, and PSNR of 58.63 dB are obtained by the developed framework for securing medical data with disease classification in healthcare systems.