Neuroimaging of Brain Tumors: Detection and Diagnosis

Abstract

Brain disorders are common in the general population. Cell growth is the primary cause of brain disorders. This has knock-on effects on the normal functioning of other vital organs, since it affects the brain. Cell proliferation leads to aggressive brain cancer. To reduce the number of deaths caused by brain tumors, early diagnosis of tumors is one of the main measures. The identification of brain tumors is accomplished by combining pictures from several sources, including computed tomography (CT) scans, magnetic resonance imaging (MRI) scans, and others, using image processing techniques. During the preprocessing step of the study, the noise in the photographs is removed. Support vector machines (SVMs) and convolutional neural networks (CNNs) are two examples of the deep learning techniques used to build the model. The goal of this study is to develop a model that can detect brain cancers using CT scan images. To evaluate the models' performance, we considered a number of metrics, such as accuracy, recall, loss, and area under the curve.