Mitigating Data Challenges and Analysis of Neuro Images Using Brain Tractography

Abstract: Neurodegenerative disorders like Alzheimer's, Parkinson's, and brain tumors are increasingly becoming challenging for healthcare systems worldwide owing to their multifaceted manifestations and delayed detection. Early diagnosis is important but is usually compromised by the dependence on specialist interpretation, sophisticated diagnostic equipment, and lengthy procedures. This article presents Brainalyze, an intelligent, web-based neuroimaging platform that streamlines and automates brain MRI analysis by integrating tractography, FA computation, and machine learning-based disease prediction into an easy-to-use interface. The platform operates on multiple imaging formats (PNG, JPEG, NIfTI), processes them with open-source tools such as DIPY and NiBabel, and classifies disease states based on a trained Convolutional Neural Network (CNN) for image data and a Random Forest classifier for structured metadata. FA values are computed to evaluate hemispheric integrity and 3D white matter streamlines are visualized using tractography. Results are displayed in the form of interactive charts and visualizations in an interactive web-based dashboard developed using Streamlit. To further improve usability and interaction, Brainalyze features NeuroBot-a chatbot AI that helps users interpret the analysis findings, provide explanations, and advise on using the system. This platform solves major accessibility, technical complexity, and interpretability of neuroimage analysis limitations. The platform is intended for clinicians, educators, and researchers who need stable, efficient, and explainable insights into brain health. With its scalable, open-source, and modular architecture, Brainalyze provides an extensive solution that caters to diagnostic as well as academic usage. The ability of the system to be deployed in the cloud and its future possibility for integration in PACS makes it a viable option for actual clinical settings and interprofessional education.

Keywords: Neuroimaging, Tractography, Machine Learning, Brain MRI, Fractional Anisotropy, AI Chatbot, Streamlit