Exploring the Role of Blockchain in Optimizing Deep Learning Models for MRI-Based Brain Tumor Detection: A Performance Evaluation

Abstract

 In recent years, the integration of blockchain technology with deep learning (DL) models has attracted significant attention within the field of medical imaging, especially for the diagnosis of brain tumors through Magnetic Resonance Imaging (MRI). This paper explores the transformative role of blockchain in optimizing deep learning models for MRI-based brain tumor detection, with a particular focus on its ability to enhance data security, integrity, and collaborative efforts across medical institutions. By evaluating the performance of blockchain-integrated deep learning models, we aim to illustrate how blockchain's decentralized and immutable features can improve the accuracy, reliability, and trustworthiness of MRI scans in detecting brain tumors. Furthermore, this paper draws upon pivotal research, particularly the work of Subrata Banik, Nani Gopal Barai, and FM Shamrat, whose contributions have significantly advanced the integration of blockchain with deep learning techniques in medical diagnostics.