Tumor Detection in Medical Images
Adrian Lopez, Leinad Rivas, Victor Palacios
Department of Mathematics
Faculty Supervisor: Sara El Alaoui
Tumor detection in medical images is a critical application of deep learning, particularly using Convolutional Neural Networks . This study explores the implementation of CNNs to identify tumors in MRI, CT, or X-ray scans. The process involves data preprocessing, model training on labeled datasets, and performance evaluation using accuracy metrics and confusion matrices. By utilizing AI-driven analysis, this method improves detection precision, expedites diagnosis, and provides valuable insights to radiologists. The integration of AI in medical imaging aims to improve diagnostic accuracy, reduce analysis time, and support radiologists in early tumor detection, ultimately contributing to better patient outcomes and streamlined medical workflows.