Multilevel Features Fusion of Intelligent Techniques for Brain Imaging Analysis

Talib A. Al-Sharify¹, Mohammed Hussein Ali ², Aqeel Hussen³, Zaid Saad Madhi⁴

¹ Computer Communication Department, Al Rafidain University College, Baghdad, Iraq

² Department of computer engineering techniques, Mazaya University college, Thi Qar, Iraq

³ Department oof medical instrument engineering techniques, Alfarahidi University, Baghdad, Iraq

⁴ Radiological Techniques Department, Al- Mustaqbal University College, 51001 Hilla, Iraq

Emails: Talib.abdzaid.elc@ruc.edu.iq ; Mohammed.hussein@mpu.edu.iq; Aqeel.hussen@alfarahidiuc.edu.iq ; zaid.saad@uomus.edu.iq,

Abstract

With the use of multi-level features fusion, this work provides a new method for recognizing cognitive brain activity, which we term the Improved Multi-modal cognitive brain-imaging method (IMCBI). Identifying brain areas and basing judgments on insights into intelligent cognitive behavior for babies and adolescents presents a number of methodological issues that the suggested approach seeks to address. In order to understand how the brain functions during various motor, perceptual, and cognitive tasks, IMCBI employs smart methods for fusing data at several levels. This technique employs functional magnetic resonance imaging (fMRI) data to assess human behavioral activity in the brain while engaging in a variety of activities. It does so by combining an inter-subject retrieval strategy with deep neural networks (DNN). The research shows that the suggested method, which uses multi-level fusion of features, greatly raises the accuracy ratio to 95.63 percent, the sensitivity to 95.42 percent, and the specificity to 94.3 three point three percent. The findings demonstrate the method's efficacy in recognizing brain activity based on high-level cognitive ability, making it a useful tool for predicting clinical and behavioral responses.

Keywords: Cognitive intelligence; Multilevel Fusion brain imaging; Neuroimaging model; function MRI; brain activity recognition.