一种用于MRI扫描中神经系统疾病准确检测的深度多尺度神经网络及实时Web部署
A Deep Multiscale Neural Network for Accurate Neurological Disorder Detection from MRI Scans and Real-Time Web Deployment
AI总结：
提出End-Net网络，通过多尺度Inception模块和WGAN-GP数据增强，实现MRI多类神经系统疾病的高精度分类，并部署为实时Web系统。
摘要：
神经系统疾病涉及大脑和神经系统的多种病理，早期准确检测至关重要。虽然许多深度CNN已被开发用于基于MRI的神经系统疾病分类，但大多数针对二分类任务优化，且常无法捕捉区分不同条件下细微解剖差异所需的多类特征。本研究提出增强型神经系统疾病检测网络（End-Net），用于神经系统疾病的多类MRI分类。End-Net包含24个卷积层，从卷积块开始，随后是21个优化的Inception模块。这些模块通过并行的1×1、3×3和分解的5×5卷积分支以及最大池化提取多尺度特征，使模型能够捕获互补的纹理、边缘、形状和上下文信息。全局平均池化头、紧凑的全连接分类器和dropout减少了参数，限制了过拟合，并提高了鲁棒性。End-Net在多类神经系统疾病数据集上进行了评估，该数据集包括阿尔茨海默病、脑肿瘤、多发性硬化症患者和健康对照组的MRI扫描。通过使用WGAN-GP增强少数类和随机欠采样多数类来解决严重的类别不平衡问题。结果表明，End-Net在准确性和泛化能力上均优于现有架构。该模型还集成到一个在线系统中，用于基于Web的实时推理和可访问性。
英文摘要：
Neurological disorders involve diverse pathologies of the brain and nervous system, making early and accurate detection essential. While many deep CNNs have been developed for MRI-based classification of neurological disorders, most are optimized for binary tasks and often fail to capture the multi-class features needed to distinguish subtle anatomical differences across conditions. This study proposes the Enhanced Neurological Disorder Detection Network (End-Net) for multi-class MRI classification of neurological disorders. End-Net includes 24 convolutional layers, beginning with convolutional blocks followed by 21 optimized inception modules. These modules extract multiscale features via parallel 1 x 1, 3 x 3, and factorized 5 x 5 convolutional branches, along with max pooling, enabling the model to capture complementary texture, edge, shape, and contextual information. A global average pooling head, compact fully connected classifier, and dropout reduce parameters, limit overfitting, and improve robustness. End-Net was evaluated on the Multi-Class Neurological Disorder dataset, comprising MRI scans from patients with Alzheimer's disease, brain tumors, multiple sclerosis, and healthy controls. Severe class imbalance was addressed by augmenting minority classes with WGAN-GP and randomly undersampling the majority class. The results show that End-Net outperforms existing architectures in both accuracy and generalization. The model is also integrated into an online system for real-time web-based inference and accessibility.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
