Hybrid Yolov8–cnn Framework For Automated Analysis Of Temporal Fmri Brain Networks

Abstract

Functional Magnetic Resonance Imaging (fMRI) Provides Critical Insights Into Dynamic Brain Activity And Connectivity Patterns, Enabling The Study Of Neural Functions Over Time. Traditional Clustering Methods, Such As Topological Data Analysis (TDA), Are Limited To Grouping Brain Networks And Cannot Accurately Detect Or Classify Active Regions Or Abnormalities Like Tumors. Manual Interpretation Of MRI And FMRI Data Is Time-consuming, Prone To Human Error, And Often Inconsistent Across Multi-site Datasets. To Overcome These Challenges, The Proposed System Introduces A Hybrid YOLOv8 And Convolutional Neural Network (CNN) Framework For Automated Detection, Localization, Classification, And Staging Of Brain Tumors Along With Analysis Of Normal Brain Activity. YOLOv8 Precisely Detects And Localizes Active Brain Regions In MRI And FMRI-derived Maps, Generating Bounding Boxes And Confidence Scores. The CNN Extracts Deep Spatial And Temporal Features Enabling Accurate Classification Of Brain States And Tumor Stage Determination. Experimental Results Demonstrate High Accuracy In Brain Activity Analysis, Tumor Detection, And Staging. This Automated Method Reduces Dependence On Manual Interpretation, Providing Faster, More Reliable, And Interpretable Clinical Insights.

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