Computational Brain Imaging Framework for Neurological Mapping and Disorder Classification Using Multimodal Image Processing

Complex neurological illnesses necessitate modern facilities’ computational methods for precise mapping and categorization of brain activities. For the diagnosis and tracking of conditions including epilepsy, Parkinson’s disease, and Alzheimer’s, precise brain mapping is essential. When it comes to diagnostic accuracy and scalability, traditional methods frequently encounter problems, including data heterogeneity, low resolution, and computational inefficiencies. Dealing with large-scale imaging datasets, enhancing the reliability of disease categorization in varied patient groups, and overcoming the limitations of cross-modality data fusion are the primary challenges. The multimodal neuro-cognitive imaging computational technique (MN-CICT) has been suggested in this research to overcome the above challenges. MN-CICT thoroughly extracts and analyses structural and functional brain data by integrating multiple imaging modalities, such as MRI, fMRI, PET, and CT. Improved resolution and interpretability of neurological mappings are achieved by the use of adaptive feature extraction, multimodal data fusion, and advanced machine learning techniques by MN-CICT. Because of its focus on computing efficiency, the framework additionally seems well suited for use in real-time scenarios. Neurodegenerative disease research, therapy planning, and clinical diagnostics are among the many areas that can benefit greatly from the framework. In comparison to current methods, the simulation results show a considerable decrease in processing time and an improvement in classification accuracy. This exemplifies its promise to enhance diagnostic results and simplify neuroimaging operations. The MN-CICT is a revolutionary method to brain imaging, which paves the way for the development of novel applications in the fields of brain–computer interfaces, customized medicine, and automated diagnostics.