Development of Hybrid AI Models for Real-Time Cancer Diagnostics Using Multi-Modality Imaging (CT, MRI, PET)

Abstract

Cancer detection and diagnosis remain some of the most critical challenges in healthcare. Advances in medical imaging, particularly multi-modality imaging technologies like CT, MRI, and PET scans, have revolutionized the ability to detect and monitor cancer. Finding and identifying cancer remains among the biggest medical issues right now. Technology that combines CT, MRI, and PET scans now makes it easier for doctors to spot and keep track of cancer. Right now, doctors struggle to make sense of intricate imaging results. This research creates combined artificial intelligence models that use various imaging results to promptly learn more about cancer. These AI models work better by combining deep learning, image segmentation, and image fusion methods to increase cancer diagnosis accuracy, find diseases early, and predict how cancer grows. The paper talks about the challenges, looks at ethical issues, and points out what might happen next when we use AI to look at cancer images.