Reshaping Brain Tumor Diagnosis: Generative AI-Enhanced Segmentation in MRI Images

Abstract

Image quality is crucial for efficient object recognition or classification rates. High-quality images lead to better outcomes than noisy and unprocessed ones, which can be challenging to extract features from, thereby reducing the rate of both processes. Pre-processing the image before feature extraction typically addresses low-quality issues, ultimately improving machine learning algorithm selected performances in terms of accuracy enhancement or training-image reduction. The research aims at highlighting a significant problem encountered within the medical industry: brain tumors; early detection has proven difficult due to MRI's susceptibility to noises and other environmental disturbances causing doctors great difficulty determining relevant causes and regions involved with tumor growths. To tackle this issue, the authors suggest adaptive brain tumor detection using image processing tools where only utilizing MRI images proves insufficient without pre-processing aid involving de-noising by median filters masking via skull selection while detailed information on one specific region would entail object labeling use coupled up with support vector machines (SVM) incorporated into an unsupervised approach maintaining future pattern creation/maintenance patterns relying heavily on texture/color-based characteristics taken over through SVM training. This system is implemented in Python.