Brain Tumor And Blockage Detection Using Deep Learning

Authors: Shaikh Mahek Javed, Udar Arati Ajay, Prof.Kemnar. K. C, Londhe Pranali Babasaheb, Kadam Rutuja Laxman, Prof.Maniyar. A. A

Abstract: Brain tumors and vascular blockages represent critical medical conditions necessitating prompt and precise diagnosis to optimize patient outcomes. Traditional manual analysis of magnetic resonance imaging (MRI) and computed tomography (CT) scans by radiologists is often time-intensive, susceptible to human error, and reliant on the availability of specialized expertise. This study proposes an innovative artificial intelligence (AI)-based system for the automated detection of brain tumors and blockages, leveraging Convolutional Neural Networks (CNNs) optimized through Particle Swarm Optimization (PSO). The developed model processes medical imaging data to accurately identify the presence, type, and severity of tumors or blockages, while providing visual localization of affected brain regions. Integrated with a user-friendly interface, the system enables healthcare professionals to upload scans and obtain immediate, detailed diagnostic outputs. By improving detection accuracy, minimizing diagnostic delays, and facilitating rapid clinical decision-making, this system enhances neurosurgical planning, improves patient outcomes, and contributes to the overall efficiency of healthcare delivery.