A Deep Multiscale Neural Network for Accurate Neurological Disorder Detection from MRI Scans and Real-Time Web Deployment

2026-06-27

AI Summary

This study addresses the challenge of capturing subtle anatomical differences in multi-class neurological disorder identification from MRI scans using deep convolutional neural networks, a task further complicated by class imbalance. To this end, the authors propose End-Net, a 24-layer multiscale deep network that integrates an enhanced Inception module—combining factorized 1×1, 3×3, and 5×5 convolutions with pooling—and a lightweight classification head. The architecture incorporates global average pooling and Dropout regularization, while leveraging WGAN-GP-based data augmentation and random undersampling to mitigate class imbalance. Evaluated on a four-class MRI dataset encompassing Alzheimer’s disease, brain tumors, multiple sclerosis, and healthy controls, End-Net significantly outperforms existing methods and enables end-to-end real-time deployment via a web interface for high-accuracy online inference.

Abstract

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.

Problem

• neurological disorder detection
• multi-class MRI classification
• multiscale feature extraction
• class imbalance
• real-time web deployment

Innovation

• multiscale feature extraction
• inception modules
• multi-class MRI classification
• WGAN-GP data augmentation
• real-time web deployment