Brain Tumor Detection And Segmentation Using Deep Learning

A brain tumor is characterized by the presence of an abnormal mass or collection of cells within the brain. Tumors can be categorized as either cancerous (malignant) or noncancerous (benign). As a prevalent and highly destructive condition, malignant brain tumors significantly shorten life expectancy if not identified and treated promptly. Classifying brain tumors is a crucial step following detection to develop an effective treatment strategy. Early diagnosis not only improves the chances of effective medical intervention but can also be life-saving. The dataset used in this study includes MRI images of brains with and without tumors. These images undergo a series of preprocessing steps, including techniques like filtering, blurring, and cropping. The processed data is then fed into a Convolutional Neural Network (CNN) model based on VGG-16 architecture to determine the presence or absence of a tumor. The model also segments MRI images to identify and isolate regions of interest for analyzing tumor detection. The classification and detection of brain tumors are provided as the final output.