Mohammad Farah, Al Ahmadi Saad, Al Muhtadi Jalal
Center of Excellence in Information Assurance (CoEIA), King Saud University, Riyadh 11543, Saudi Arabia.
College of Computer & Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia.
Diagnostics (Basel). 2023 Mar 24;13(7):1229. doi: 10.3390/diagnostics13071229.
Brain tumors are nonlinear and present with variations in their size, form, and textural variation; this might make it difficult to diagnose them and perform surgical excision using magnetic resonance imaging (MRI) scans. The procedures that are currently available are conducted by radiologists, brain surgeons, and clinical specialists. Studying brain MRIs is laborious, error-prone, and time-consuming, but they nonetheless show high positional accuracy in the case of brain cells. The proposed convolutional neural network model, an existing blockchain-based method, is used to secure the network for the precise prediction of brain tumors, such as pituitary tumors, meningioma tumors, and glioma tumors. MRI scans of the brain are first put into pre-trained deep models after being normalized in a fixed dimension. These structures are altered at each layer, increasing their security and safety. To guard against potential layer deletions, modification attacks, and tempering, each layer has an additional block that stores specific information. Multiple blocks are used to store information, including blocks related to each layer, cloud ledger blocks kept in cloud storage, and ledger blocks connected to the network. Later, the features are retrieved, merged, and optimized utilizing a Genetic Algorithm and have attained a competitive performance compared with the state-of-the-art (SOTA) methods using different ML classifiers.
脑肿瘤是非线性的,其大小、形态和纹理存在差异;这可能使得利用磁共振成像(MRI)扫描对其进行诊断和手术切除变得困难。目前可用的程序由放射科医生、脑外科医生和临床专家执行。研究脑部MRI既费力、容易出错又耗时,但在脑细胞的情况下它们仍显示出较高的定位准确性。所提出的卷积神经网络模型,一种现有的基于区块链的方法,用于保护网络以精确预测脑肿瘤,如垂体瘤、脑膜瘤和胶质瘤。脑部的MRI扫描在归一化到固定维度后首先输入到预训练的深度模型中。这些结构在每一层都会发生改变,提高其安全性。为防止潜在的层删除、修改攻击和篡改,每一层都有一个额外的块来存储特定信息。多个块用于存储信息,包括与每一层相关的块、保存在云存储中的云账本块以及连接到网络的账本块。之后,利用遗传算法检索、合并和优化这些特征,并且与使用不同机器学习分类器的最新技术(SOTA)方法相比,已经获得了有竞争力的性能。