AI-based System for Brain Tumor Segmentation and 3D Visualization

Computer vision is a branch of artificial intelligence that enables computers to learn patterns and extract useful information from various visual content. In healthcare, advances in computer vision models have been employed to provide accurate analysis of medical images, aiding tasks such as disease detection, human organ segmentation, and medical image classification. Such systems have the potential to enhance healthcare by improving patient outcomes and enabling early and accurate detection of diseases. The 3D CATBraTS is a novel deep learning architecture designed for three-dimensional segmentation of brain tumors on various magnetic resonance imaging (MRI) acquisitions. This paper presents a real-world web application capable of automatically detecting and accurately locating brain tumors on medical images. The proposed tool integrates the 3D CATBraTS as the backend deep learning model to improve its applicability and usability. The web application empowers medical professionals to perform segmentations seamlessly, even without coding skills, resulting in an improved user experience.