Deep Neural Network for Brain Tumour Segmentation Using Guaranteed Time Slots (GTS) Algorithm

Abstract

Segmentation of brain tumors is a crucial step in the field of medical imaging, essential for the early identification and treatment of brain tumors. This process significantly influences patient care by facilitating timely intervention. Brain tumors originate from abnormal cell growth and proliferation that diverge from normal cell behavior, resulting in tumor development. Segmentation, the differentiating between normal brain tissue and tumor regions, is critical for precise diagnosis and effective treatment strategy. Historically, the task of segmentation has been manually performed by healthcare experts. This method is labor-intensive and difficult due to the intricate and diverse brain structure. Deep neural networks (DNNs), renowned for their prowess in image classification tasks, have been employed to overcome these obstacles. Specifically, the GTS (graph-based transductive segmentation) algorithm has been introduced to optimize magnetic resonance imaging (MRI) image segmentation. This advanced method enhances the efficiency of finding the closest optimal solutions by improving convergence, thereby significantly reducing the time required for solution identification. The GTS technique stands out for its remarkable accuracy rate of 97%, showcasing some substantial improvement over previous segmentation methods. This leap in accuracy expedites the diagnostic process and increases the likelihood of successful patient outcomes by enabling earlier and more precise tumor detection.