Brain Tumor Detection Using Deep Learning-Based R-CNN

Medical image processing offers a difficult task of brain tumor detection. Deep learning-based techniques have recently demonstrated promising results in the detection and classification of tumors in the brain in magnetic resonance imaging (MRI). In this research, we present a deep learning-based method for employing R-CNN to detect brain cancers with high precision. The purpose of this research is to improve the accuracy and efficiency of diagnosing brain tumors so that patients can receive the early detection and treatment they require. The suggested method involves using a huge dataset of MRI brain scans to train a convolutional neural network (CNN) to identify probable tumor-containing areas of interest (ROI). The ROI are then fed into a region proposal network (RPN), which generates a set of bounding boxes around the potential tumors. Finally, the bounding boxes are classified using a CNN to determine whether they contain a tumor or not.