MIND: An Adaptive Multimodal Fusion Framework for Integrated Neurological Diagnosis

Abstract

Accurate computational mapping and classification of brain activities are essential for diagnosing and monitoring intricate neurological disorders such as epilepsy, Parkinson's disease, and Alzheimer's disease. But traditional methods are limited in their diagnostic accuracy and scalability because they deal with data that is not uniform, has low resolution, and is not very efficient at computing. This paper proposes the Multimodal Integrated Neurological Diagnosis (MIND) Framework, a new framework that aims to get around these problems. MIND combines structural and functional data from MRI, fMRI, PET, and CT scans using adaptive feature extraction, advanced data fusion, and machine learning models that work best. The framework greatly improves the resolution, ease of understanding, and speed of neurological mappings. In comparative simulations, MIND gets 93.7% of the classifications right, cuts processing time down to 12 seconds (an improvement of 22.5% over the baseline), and gets 98.6% of the cross-modality fusions right. It also shows that it can handle a wide range of patient groups better. These results show that MIND is a strong and effective tool for planning treatments and making clinical diagnoses. The framework's ability to process data in real time and with high accuracy opens the door to more advanced uses in personalized medicine, automated diagnostics, and brain-computer interfaces.