Adaptive Neuro-Fuzzy Inference System (ANFIS) for Enhanced 3D Brain Reconstruction from MRI Scans

The rapidly developing field of medical imaging is always looking to improve the precision and effectiveness of 3D brain reconstruction, which is essential for making accurate diagnoses and developing treatment plans. This research presents a novel method utilizing the Adaptive Neuro-Fuzzy Inference System (ANFIS) to improve the process of reconstructing three-dimensional brain structures from MRI scans. Conventional techniques, however successful, frequently encounter difficulties such as heightened susceptibility to noise and restricted level of detail, which adversely affect the accuracy of diagnoses. The suggested ANFIS framework combines the advantages of neural networks and fuzzy logic, providing a strong and effective solution to these difficulties. Our methodology encompasses a thorough procedure of obtaining, preparing, and using the ANFIS model, specifically tailored for analysing MRI data. The results indicate a notable enhancement in the quality of reconstruction, as demonstrated by improved image resolution and decreased noise interference in comparison with traditional methods. The provided enhancements are validated by quantitative indicators, which demonstrate increased accuracy and improved computing efficiency. This work not only facilitates the development of more precise and comprehensive brain imaging techniques, but also creates opportunities for utilizing ANFIS in other intricate medical imaging assignments.