Comparison of Brain Tumor Detection in MRI Images Using Straightforward Image Processing Techniques and Deep Learning Techniques

A brain tumor is a mass or development of atypical cells inside the skull region of the brain. The growth of such malignancy in a confined space leads to a cohort of problems, like the malfunctioning of the brain. The tumor can be malignant or benign, and early detection might turn out to be a savior. For this purpose, computerized tomography (CT) scans and magnetic resonance imaging (MRI) scans are examined. In recent decades' image processing, computer vision and deep learning approaches have gained substantial recognition. However, straightforward approaches using image enhancement techniques and morphological operations are also much efficient in this regard, such an image processing approach is compared to the state-of-the-art deep learning techniques in this paper for detecting a tumor in the MRI scans of the brain. The straightforward system is incorporated into four steps. First, the scan is pre-processed for adjustment of its quality. Second, the image is enhanced using image enhancement approaches. Third, edge detection approaches are applied to it. Fourth, image segmentation with morphological operators is applied to detect the tumor region. The findings are then compared with the results of previous deep learning techniques. The purpose of this study is to present that advanced deep learning algorithms can generate better results and perform multiple classifications of brain tumor detection in MRI images.