Deep Learning Framework for Early Prediction of Neurological Disorders Using MRI-Based Feature Extraction

Abstract

Early detection of neurological disorders such as Alzheimer's (AD), Parkinson's (PD) and schizophrenia (SZ) disease is crucial for effective clinical intervention and disease management. Traditional diagnostic methods based on manual neuroimaging reads are time-consuming, subjective and of limited value in catching subtle pathological changes during early stage disease. In this study, we propose a hybrid automated framework that integrates a customized 3D convolutional neural network (3D-CNN) for deep feature extraction from volumetric MRI scans with machine learning classifiers such as Support Vector Machine (SVM)s., Random Forest (RF) s, XGBoost s, Multilayer Perceptron (MLP)s etc. —for accurate disease classification.

The framework was evaluated on three benchmark neuroimaging datasets: ADNI (named after the Alzheimer's Disease Neuroimaging Initiative, which provides images of AD and Mild Cognitive Impairment (MCI)), PPMI for PD, COBRE for SZ. Preprocessing included skull stripping, normalization and spatial alignment to ensure that all datasets were comparable in structure. Experimental outcomes suggest that the hybrid approach outperforms conventional models; achieving peak accuracy 92.7% for AD, 89.4% for PD and 85.8% for SZ. XGBoost scores the highest Area Under the Curve (AUC) across all tasks tested. Its ability to generalize across multiple disorders, while still maintaining high accuracy and interpretability, provide evidence that it could become a scalable clinical decision-support tool.

This research is a cost-effective tool for early-stage diagnosis of complex neurological diseases that blends deep learning methods such as 3D-CNNs and Random Forests with traditional machine learning techniques like SVMs. It.Qumpt(i) ope8vides better than any previous solution results across various datasets and diseases.