Brain Tumor Detection and Diagnosis using YOLO (V8) in Deep Learning

Abstract: The advent of advanced healthcare software systems presents a promising avenue for revolutionizing the early detection and management of brain tumors, a critical aspect of modern healthcare. This project delves into the development of such a system, leveraging cutting-edge technologies to enhance the efficiency and effectiveness of brain tumor diagnosis and patient care. At its core, the system harnesses the power of the YOLO (V8) algorithm to enable precise detection of tumors from MRI scans, providing clinicians with invaluable insights into patient health. Moreover, the software facilitates seamless communication between patients and healthcare facilities, streamlining processes such as appointment scheduling and confirmation in real-time. Built on a robust software architecture comprising React for the frontend and Python (Flask) and .Net (6.0) for backend functionalities, the system offers an intuitive user interface that empowers users to upload MRI scans, schedule appointments, and visualize tumor detection results with ease. Integration with Firebase ensures secure user authentication, enhancing the privacy and security of patient data. By amalgamating these technologies, this project endeavors to create a user-friendly, efficient, and integrated healthcare solution that prioritizes timely diagnosis and improved patient care. The overarching goal is to address the pressing need for early detection and management of brain tumors, ultimately contributing to better health outcomes for patients worldwide.

Keywords: Brain tumor detection, MRI scan, DL, Patient engagement, Appointment scheduling, User authentication.