APPLICATION OF CONVOLUTIONAL NEURAL NETWORKS IN CLASSIFICATION OF GBM FOR ENHANCED PROGNOSIS

Abstract

The lethal brain tumor “Glioblastoma” has the propensity to grow over time. To improve patient outcomes, it is essential to classify GBM accurately and promptly in order to provide a focused and individualized treatment plan. Despite this, deep learning methods, particularly Convolutional Neural Networks (CNNs), have demonstrated a high level of accuracy in a myriad of medical image analysis applications as a result of recent technical breakthroughs. The overall aim of the research is to investigate how CNNs can be used to classify GBMs using data from medical imaging to improve prognostic precision and effectiveness. This research study will demonstrate a suggested methodology that uses CNN architecture and is trained using a database of MRI pictures of this tumor. The constructed model will be assessed based on its overall performance. Extensive experiments and comparisons with conventional machine learning techniques and existing classification methods will also be made. It is crucial to emphasize the possibility of early and accurate prediction in a clinical workflow because it can substantially impact treatment planning and patient outcomes. The paramount objective is to address the classification challenge and outline a clear pathway toward enhancing prognosis precision and treatment effectiveness.