An Intelligent System to Analyze the Functional Magnetic Resonance Imaging fMRI

A Lot of medical projects aim to combine biology with computer science like artificial limb which is able to simulate real limb's activities to some extent, and that requires to comprehend the neurological map of the brain. The best way to measure the brain's activity is Functional Magnetic Resonance Imaging (fMRI), where it is a functional neuroimaging procedure using MRI technology that measures brain activity by detecting changes associated with blood flow. In this paper we develop an automatic system based on soft computing methods, to analyze fMRI Images and conclude their proper intended behavior. Our data was composed from two parts, the major part was obtained from the famous dataset (A test-retest fMRI dataset for motor, language and spatial attention functions), which has a representation of five different behaviors “finger foot and lip movement, overt verb generation, covert verb generation, overt word repetition and landmark tasks”, where the second part was prepared by us using images that free downloaded from internet network. Our developed automatic classification system is based on neural network framework, which is proceeding in two stages: 1. The first stage extracts four specific features, through applying sophisticated techniques for automatic image processing and analysis, related to the presence of different intensity values and their addresses over the 2 dimensions studied images. The selected features were unique and contribute to make our system, good represented. 2. The second stage is a classification technique, through designing a suitable artificial intelligence system architecture and learning algorithm. We did a lot of experiments in order to select the best neural network architecture and training method, the experiments proved that the best performance was achieved in three layers neural network: input, hidden and output layers, with a training method based on Back propagation algorithm, and sigmoid activation function. Developed system achieved an accuracy of 94.4%