Enhanced neurological anomaly detection in MRI images using deep convolutional neural networks

Area of Science:

• Medical Imaging
• Artificial Intelligence
• Neurology

Background:

• Neurodegenerative diseases present diagnostic challenges due to complexity and manual interpretation limitations.
• Current methods for diagnosing conditions like Parkinson's, Alzheimer's, and epilepsy are time-consuming and variable.
• Automating neuro-diagnostics is crucial for timely and accurate patient care.

Purpose of the Study:

• To develop and evaluate a deep learning framework for automated detection and classification of neurological anomalies in MRI data.
• To address the limitations of manual interpretation in neuro-diagnostics.
• To enhance the accuracy and efficiency of diagnosing neurodegenerative diseases.

Main Methods:

• A specialized deep convolutional neural network (DCNN) was designed for MRI analysis.
• Preprocessing included noise reduction and intensity normalization of MRI scans.
• The DCNN utilized ReLU activation, Adam optimizer, and random search for hyper-parameter tuning, with cross-fold validation for reliability.

Main Results:

• The proposed DCNN achieved a classification accuracy of 98.44% on MRI data.
• Performance metrics (precision, recall, F1-score) confirmed the model's robustness, outperforming ResNet-50 and AlexNet.
• Statistical analyses validated the significant performance improvements of the DCNN framework.

Conclusions:

• The developed DCNN framework represents a significant advancement in automated neuro-diagnostics.
• High accuracy in detecting neurological anomalies can improve diagnostic workflows and support personalized treatment strategies.
• Further research integrating multimodal data is recommended to enhance clinical utility and assess performance across diverse scenarios.