Brain Tumor Segmentation and Detection Using Machine Learning

Abstract

The Brain Tumor Segmentation and Detection System is an advanced system that aims to help in the diagnosis of brain tumors using deep learning techniques including VGG16 and U-Net architectures. Automatic segmentation, detection, classification, and localization of brain tumors in MRI scans are provided by these models, which reduce diagnostic time and improve accuracy. This makes interpretation by radiologists less reliant on manual interpretation, thus aiding in detecting tumors more quickly and in a more reliable manner. Conventional techniques of diagnosing brain tumors rely mostly on subjective determination of MRIs by radiologists, which can cause inconsistencies, delay, and mistakes. With the increased number of MRI data in clinical practice, the burden of radiologists increases, thus creating diagnosis bottlenecks. Such difficulties require automatic, effective, and scalable solutions that can help improve diagnostic accuracy while reducing delays and ultimately improving patient care. The proposed system addresses these challenges by automating key processes in the diagnosis procedure, including tumor segmentation and MRI spot identification. It features a complete ecosystem that includes mobile apps for patients and doctors, along with a web panel for administrators. The patient app allows users to upload MRI scans and receive tumor analysis through AI assistance. The doctor app enables appointment management and consultations. The system delivers accurate and immediate results, which enhance the healthcare experience for both patients and medical personnel.