DefinePK

Abstract

Neurological disorders pose significant challenges in medical diagnostics due to their complex manifestations and overlapping symptoms. Accurate and early diagnosis is crucial for effective treatment planning and improved patient outcomes. Traditional diagnostic methods rely heavily on manual interpretation of MRI scans, which can be time-consuming and prone to interobserver variability. Recent advancements in artificial intelligence (AI) and deep learning have demonstrated promising results in automating medical image analysis. However, single deep learning models often struggle with generalization across diverse datasets, leading to suboptimal performance. To address these challenges, an AI-driven Ensemble Deep Neural Network (DNN) framework is proposed for the automated classification of neurological disorders from MRI scans. The framework integrates multiple deep learning architectures, including Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, and Transformer-based vision models, to enhance feature extraction and classification accuracy. A weighted averaging mechanism is employed to optimize predictions from individual models, ensuring robustness and reliability. The dataset is preprocessed using intensity normalization and augmentation techniques to improve generalizability. Experimental evaluation on benchmark neurological MRI datasets shows the superiority of the proposed ensemble framework over traditional deep learning models. The approach achieves 98.5% classification accuracy, outperforming existing CNN-based architectures. Additionally, the ensemble model exhibits improved sensitivity and specificity, making it a reliable tool for assisting radiologists in diagnosing conditions such as Alzheimer’s disease, Parkinson’s disease, and brain tumors. By leveraging ensemble deep learning, the proposed framework enhances diagnostic precision and reduces reliance on manual assessment. This AI-driven system has the potential to revolutionize neurological disorder diagnosis, facilitating early detection and personalized treatment strategies.