A Review of Technical Coherence between Brain Tumors and their Diagnostic Imaging Spectra

In recent years, early identification of brain tumors has become a major topic of research. Early detection of a tumor for initial therapy enhances the likelihood of the victims life span. Computing Magnetic Resonance Imaging (MRI) for prior tumor identification has the dispute of high computing overhead due to the large volume of image input to the computing system. As a result, there was a significant delay and a drop in system efficiency. As a result, the demand for a more advanced detection system that can accurately segment and represent data for quicker and more precise computing has grown in the latest years. In recent literatures, new methodologies for brain tumor detection based on better learning and processing have been proposed. This study provides a brief overview of recent advances in the field of MRI computing for prompt identification and diagnosis of brain tumors, including representation, segmentation and the application of novel Image Processing and Artificial Intelligence (AI) approaches in analyzing. The present tendency in brain tumor detection computerization, as well as the benefits, limitations, and prospects of existing systems for computer aided diagnostics in detection of brain tumor, are discussed.