Glioma brain tumor detection and diagnosis using CIFC EVGGCNN and enhanced visual geometry group deep learning structure

Abstract

Brain tumor detection is a crucial medical task that involves newlineidentifying abnormal regions within the brain. Traditionally, this has been newlineaccomplished through invasive procedures, such as inserting foreign objects into newlinethe brain to locate tumors. These methods are not only time-consuming but also newlinecause significant pain and discomfort for patients, often leading to blood loss. newlineTo address these limitations and improve patient experience, a non-invasive newlineapproach for brain tumor detection and localization has been proposed. newlineThis method utilizes scanning techniques, specifically Computer Tomography newline(CT) and Magnetic Resonance Imaging (MRI). This thesis focuses on the newlineapplication of MRI for tumor region detection and segmentation. newlineBy exploring the potential of non-invasive techniques like MRI, we newlineaim to transform brain tumor detection, making it more patient-friendlier, newlineefficient, and accurate. Through this research, we hope to contribute to the newlineadvancement of medical imaging technology and ultimately improve healthcare newlineoutcomes for individuals with brain tumors. newlineThe methodologies presented in this study have been applied to newlinepublicly accessible brain MRI images and assessed for their performance. newlineTo gauge the efficacy of the proposed system in detecting and diagnosing brain newlinetumors, the simulation outcomes were contrasted with those of traditional newlinemethods, considering sensitivity, specificity, and accuracy. newline