Brain Tumor Segmentation and Genetic Biomarker Prediction Using Machine Learning Techniques

Abstract

Brain tumors, a significant global health challenge, affect individuals of all ages and newlineimpose considerable physical, cognitive, and economic burdens. Accurate detection, newlinesegmentation, and classification of these tumors in MRI images are crucial for effective newlinetreatment planning and patient care. These methods are crucial for accurate diagnostics newlinedue to the diverse shapes and sizes of tumors, with precise MRI segmentation essential newlinefor evaluating disease progression, planning surgeries, and assessing patient outcomes. newlineTraditional manual methods often fail to define tumor boundaries accurately due to noise newlineand irregular shapes. These methods are computationally intensive, causing delays and newlinereducing diagnostic efficiency. Conventional systems often miss subtle abnormalities or newlinemisclassify lesions due to limitations in feature extraction and imbalanced datasets. newlineThis research aims to develop a flexible and effective brain tumor segmentation system newlineto accurately identify tumor regions from MRI scans and measure tumor size, aiding in newlinetreatment planning. This will be achieved by integrating GWO and CS algorithms within newlinea FCM framework. The research aims to accurately detect and classify tumors using newlinemachine and deep learning techniques. EfficientDet will be employed for efficient object newlinedetection, while Vision Transformers, reinforcement learning, graph neural networks, newlineand LSTM will be integrated for classification to enhance feature extraction and newlineclassification accuracy. newlineGlioblastoma, a severe brain cancer with a poor prognosis, is associated with the MGMT newlinepromoter methylation marker, which is linked to a better response to chemotherapy. The newlinegenetic characteristics of cancer are currently diagnosed through invasive tissue sampling newlineand lengthy testing, but a non-invasive method using radiogenomics is necessary to newlinepredict genetic profiles, potentially reducing the need for multiple surgeries and aiding in newlinetailored treatments. The research aims to predict the status of the MGMT promoter newlinemethylation, a key genetic bioma